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Journal articles on the topic 'Attentional Network Theory'
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1
Chou, Kenny F., and Kamal Sen. "AIM: A network model of attention in auditory cortex." PLOS Computational Biology 17, no. 8 (August 27, 2021): e1009356. http://dx.doi.org/10.1371/journal.pcbi.1009356.
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Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.
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Cohen, Noga, Avishai Henik, and Nilly Mor. "Can Emotion Modulate Attention? Evidence for Reciprocal Links in the Attentional Network Test." Experimental Psychology 58, no. 3 (November 1, 2011): 171–79. http://dx.doi.org/10.1027/1618-3169/a000083.
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Evolution theory suggests that adaptive behavior depends on our ability to give preferential attention to emotional information when it is necessary for our survival, and to down-regulate irrelevant emotional influence. However, empirical work has shown that the interaction between emotion and attention varies, based on the attentional network in question. The aim of the current research was to examine the influence of stimulus emotionality on attention in three attentional networks: alerting, orienting, and executive functions. In two studies, using negative and neutral cues in a modified version of the Attention Network Test, it was found that negative cues impaired task performance in the absence of executive conflict, but not when executive processes were activated. Moreover, it was found that the influence of negative cues on task performance in a given trial was attenuated following activation of executive processes in the previous trial. These results suggest that when executive resources are required, inhibitory mechanisms are recruited to decrease the disruptive effect of emotional stimuli. More importantly, these findings indicate that the effect of emotional stimuli on attention is down-regulated both during cognitive conflict and after the conflict has already ended.
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Li, Yu, Yuan Tian, Jiawei Zhang, and Yi Chang. "Learning Signed Network Embedding via Graph Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4772–79. http://dx.doi.org/10.1609/aaai.v34i04.5911.
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Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.
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Fitzhugh, Megan C., B. Blair Braden, Marwan N. Sabbagh, Corianne Rogalsky, and Leslie C. Baxter. "Age-Related Atrophy and Compensatory Neural Networks in Reading Comprehension." Journal of the International Neuropsychological Society 25, no. 6 (April 29, 2019): 569–82. http://dx.doi.org/10.1017/s1355617719000274.
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AbstractObjectives: Despite changes to brain integrity with aging, some functions like basic language processes remain remarkably preserved. One theory for the maintenance of function in light of age-related brain atrophy is the engagement of compensatory brain networks. This study examined age-related changes in the neural networks recruited for simple language comprehension. Methods: Sixty-five adults (native English-speaking, right-handed, and cognitively normal) aged 17–85 years underwent a functional magnetic resonance imaging (fMRI) reading paradigm and structural scanning. The fMRI data were analyzed using independent component analysis to derive brain networks associated with reading comprehension. Results: Two typical frontotemporal language networks were identified, and these networks remained relatively stable across the wide age range. In contrast, three attention-related networks showed increased activation with increasing age. Furthermore, the increased recruitment of a dorsal attention network was negatively correlated to gray matter thickness in temporal regions, whereas an anterior frontoparietal network was positively correlated to gray matter thickness in insular regions. Conclusions: We found evidence that older adults can exert increased effort and recruit additional attentional resources to maintain their reading abilities in light of increased cortical atrophy.
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Craig, Curtis M., and Martina I. Klein. "The Abbreviated Vigilance Task and Its Attentional Contributors." Human Factors: The Journal of the Human Factors and Ergonomics Society 61, no. 3 (January 25, 2019): 426–39. http://dx.doi.org/10.1177/0018720818822350.
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Objective: To measure contributing attentional processes, particularly that of executive attention, to two iterations of the abbreviated vigilance task. Background: Joel Warm was at the forefront of vigilance research for decades, and resource theory is currently the dominant explanation for the vigilance decrement. The underlying mechanisms contributing to both overall performance and the decrement are only partly understood. Method: Seventy-eight participants answered questionnaires about their attentional skills and stress state, performed the Attention Network Test and two blocks of the 12-min abbreviated vigilance task, with a brief break between the two vigils during which they viewed images intended to affect performance. Changes in oxygenated hemoglobin were measured with functional near-infrared imaging. Results: Expected patterns were observed for both iterations of the abbreviated vigilance task, with performance declining after the first 2 min. Manipulations intended to evaluate whether executive processes contributed to vigilance performance failed to observe an effect. Other factors, particularly orienting and alerting attentional networks, task engagement, and subclinical ADHD symptomology were associated with performance. Significant factors for the first and second vigilance blocks were different. Conclusion: We suggest that (a) cognitive control is not a predominant factor, at least for the abbreviated vigilance task, and (b) attentional mechanisms and stress states affecting performance on the abbreviated vigilance task change over time. Application: Potential applications of this research include the use of breaks for sustained attention tasks involving high sensory load, and implications for the use of the abbreviated vigilance task as a proxy for general vigilance processes.
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MAX, JEFFREY E., DONALD A. ROBIN, H. GERRY TAYLOR, KEITH O. YEATES, PETER T. FOX, JACK L. LANCASTER, FACUNDO F. MANES, KATHERINE MATHEWS, and SHANNON AUSTERMANN. "Attention function after childhood stroke." Journal of the International Neuropsychological Society 10, no. 7 (November 2004): 976–86. http://dx.doi.org/10.1017/s1355617704107066.
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We investigated attentional outcome after childhood stroke and orthopedic diagnosis in medical controls. Twenty-nine children with focal stroke lesions and individually matched children with clubfoot or scoliosis were studied with standardized attention and neuroimaging assessments. Stroke lesions were quite varied in location and commonly involved regions implicated in Posner's model of attention networks. Children with stroke lesions performed significantly more poorly regarding attention function compared with controls. Performance on the Starry Night, a test demanding alerting and sensory-orienting but not executive attention function, was significantly associated with lesion size in the alerting and sensory-orienting networks but not the executive attention network. Furthermore, earlier age at lesion acquisition was significantly associated with poorer attention function even when lesion size was controlled. These findings support the theory of dissociable networks of attention and add to evidence from studies of children with diffuse and focal brain damage that early insults are associated with worse long-term outcomes in many domains of neuropsychological function. In addition, these results may provide clues towards the understanding of mechanisms underlying attention in children. (JINS, 2004,10, 976–986.)
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Li, Ying, Jia-Jie Xu, Peng-Peng Zhao, Jun-Hua Fang, Wei Chen, and Lei Zhao. "ATLRec: An Attentional Adversarial Transfer Learning Network for Cross-Domain Recommendation." Journal of Computer Science and Technology 35, no. 4 (July 2020): 794–808. http://dx.doi.org/10.1007/s11390-020-0314-8.
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Duecker, Felix, Elia Formisano, and Alexander T. Sack. "Hemispheric Differences in the Voluntary Control of Spatial Attention: Direct Evidence for a Right-Hemispheric Dominance within Frontal Cortex." Journal of Cognitive Neuroscience 25, no. 8 (August 2013): 1332–42. http://dx.doi.org/10.1162/jocn_a_00402.
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Lesion studies in neglect patients have inspired two competing models of spatial attention control, namely, Heilman's “hemispatial” theory and Kinsbourne's “opponent processor” model. Both assume a functional asymmetry between the two hemispheres but propose very different mechanisms. Neuroimaging studies have identified a bilateral dorsal frontoparietal network underlying voluntary shifts of spatial attention. However, lateralization of attentional processes within this network has not been consistently reported. In the current study, we aimed to provide direct evidence concerning the functional asymmetry of the right and left FEF during voluntary shifts of spatial attention. To this end, we applied fMRI-guided neuronavigation to disrupt individual FEF activation foci with a longer-lasting inhibitory patterned TMS protocol followed by a spatial cueing task. Our results indicate that right FEF stimulation impaired the ability of shifting spatial attention toward both hemifields, whereas the effects of left FEF stimulation were limited to the contralateral hemifield. These results provide strong direct evidence for right-hemispheric dominance in spatial attention within frontal cortex supporting Heilman's “hemispatial” theory. This complements previous TMS studies that generally conform to Kinsbourne's “opponent processor” model after disruption of parietal cortex, and we therefore propose that both theories are not mutually exclusive.
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McGrew, Kevin S., W. Joel Schneider, Scott L. Decker, and Okan Bulut. "A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”." Journal of Intelligence 11, no. 1 (January 16, 2023): 19. http://dx.doi.org/10.3390/jintelligence11010019.
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For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9–19 standardization sample of the Woodcock–Johnson Tests of Cognitive Ability—Fourth Edition. Results support the primary broad abilities from the Cattell–Horn–Carroll (CHC) theory and suggest that the working memory–attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.
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Chen, Feiyang, Ying Jiang, Xiangrui Zeng, Jing Zhang, Xin Gao, and Min Xu. "PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation." Algorithms 13, no. 5 (May 19, 2020): 126. http://dx.doi.org/10.3390/a13050126.
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Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings, and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method.
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Guo, Xiangyu. "A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network." Computational Intelligence and Neuroscience 2022 (May 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/5283439.
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In the field of education, the development of educational big data has become an important strategic choice to promote the construction of the digital campus and educational reform, and educational big data has become a new driving force in the field of education that cannot be ignored. Based on the theoretical basis of output-driven hypothesis neural network, and combining the media spanning of contemporary art and cross-media association effect, this study changes the status quo of English teaching through traditional methods such as grammar-translation method and deductive method and constructs a new cross-media university English teaching model. Based on the existing feature learning model of two-way attention, combined with existing techniques such as generative adversarial networks and semantic hashing, the semantic association between different media data is deeply mined, and feature learning is integrated with adversarial learning and hash learning to build a unified semantic space for different media data. In this paper, we focus on the structure and characteristics of convolutional neural networks through the study of deep learning theory, discuss three classical convolutional neural network models, such as AlexNet, VGG, and GoogLeNet, and propose a convolutional neural network model applicable to cross-media teaching in college English classroom and carry out experimental validation, and the results show that the proposed neural network model is based on output-driven hypothesis. The following research has been added to the abstract: to address the key problem of the semantic gap that is difficult to cross in cross-media semantic learning, a cross-media supervised adversarial hashing model based on two-way attentional features is proposed. Based on the existing two-way attention-based feature learning model, we combine existing techniques such as generative adversarial networks and semantic hashing to deeply explore the semantic association between different media data and integrate feature learning with adversarial learning and hashing to build a unified semantic space for different media data. The results show that the proposed neural network model of cross-media teaching in college English classrooms based on the output-driven hypothesis can not only promote the improvement of students’ English literacy skills but also have a certain promotion effect on their overall performance improvement.
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Yang, Yihao, Howard Gritton, Martin Sarter, Sara J. Aton, Victoria Booth, and Michal Zochowski. "Theta-gamma coupling emerges from spatially heterogeneous cholinergic neuromodulation." PLOS Computational Biology 17, no. 7 (July 30, 2021): e1009235. http://dx.doi.org/10.1371/journal.pcbi.1009235.
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Theta and gamma rhythms and their cross-frequency coupling play critical roles in perception, attention, learning, and memory. Available data suggest that forebrain acetylcholine (ACh) signaling promotes theta-gamma coupling, although the mechanism has not been identified. Recent evidence suggests that cholinergic signaling is both temporally and spatially constrained, in contrast to the traditional notion of slow, spatially homogeneous, and diffuse neuromodulation. Here, we find that spatially constrained cholinergic stimulation can generate theta-modulated gamma rhythms. Using biophysically-based excitatory-inhibitory (E-I) neural network models, we simulate the effects of ACh on neural excitability by varying the conductance of a muscarinic receptor-regulated K+ current. In E-I networks with local excitatory connectivity and global inhibitory connectivity, we demonstrate that theta-gamma-coupled firing patterns emerge in ACh modulated network regions. Stable gamma-modulated firing arises within regions with high ACh signaling, while theta or mixed theta-gamma activity occurs at the peripheries of these regions. High gamma activity also alternates between different high-ACh regions, at theta frequency. Our results are the first to indicate a causal role for spatially heterogenous ACh signaling in the emergence of localized theta-gamma rhythmicity. Our findings also provide novel insights into mechanisms by which ACh signaling supports the brain region-specific attentional processing of sensory information.
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Cheng, Yi, and Xiuli Ma. "scGAC: a graph attentional architecture for clustering single-cell RNA-seq data." Bioinformatics 38, no. 8 (February 17, 2022): 2187–93. http://dx.doi.org/10.1093/bioinformatics/btac099.
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Abstract Motivation Emerging single-cell RNA sequencing (scRNA-seq) technology empowers biological research at cellular level. One of the most crucial scRNA-seq data analyses is clustering single cells into subpopulations. However, the high variability, high sparsity and high dimensionality of scRNA-seq data pose lots of challenges for clustering analysis. Although many single-cell clustering methods have been recently developed, few of them fully exploit latent relationship among cells, thus leading to suboptimal clustering results. Results Here, we propose a novel unsupervised clustering method, scGAC (single-cell Graph Attentional Clustering), for scRNA-seq data. scGAC firstly constructs a cell graph and refines it by network denoising. Then, it learns clustering-friendly representation of cells through a graph attentional autoencoder, which propagates information across cells with different weights and captures latent relationship among cells. Finally, scGAC adopts a self-optimizing method to obtain the cell clusters. Experiments on 16 real scRNA-seq datasets show that scGAC achieves excellent performance and outperforms existing state-of-art single-cell clustering methods. Availability and implementation Python implementation of scGAC is available at Github (https://github.com/Joye9285/scGAC) and Figshare (https://figshare.com/articles/software/scGAC/19091348). Supplementary information Supplementary data are available at Bioinformatics online.
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Nelson, Robert. "Vigilance, expectancy, and noise: Attention in second language lexical learning and memory." Second Language Research 27, no. 2 (March 2, 2011): 153–71. http://dx.doi.org/10.1177/0267658310385757.
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Talamas et al. (1999), Ferré et al. (2006) and Sunderman and Kroll (2006) exposed participants to first-language/second-language (L1/L2) pairs of words and asked them to decide whether the second word was the correct translation of the first. In the critical condition, the L2 word was either the translation of the L1 word ( man → hombre) or a form-relative of the translation ( man → hambre). Less fluent speakers showed higher recognition latencies in the form-relative condition than did more fluent speakers. This report explores whether an appropriately trained Adaptive Resonance Theory (ART) neural network (Carpenter and Grossberg, 1987a) will suffer from form-relative interference, and the role of vigilance (a parameter of low-level attention sensitive to environmental complexity) in this effect. I argue that the learning environment of early bilinguals is more complex than that of adult L2 learners, and therefore adult learners may be less vigilant to word form. ART2 networks were trained with English and Spanish corpora under conditions emulating early and late second language acquisition at different vigilance levels and then serially exposed to the same types of word pairs used in the three studies mentioned above. Form-relative interference was observed, indicating that low-level attentional mechanisms may play a role in second language lexical learning and access.
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Hayes, Scott M., Norbou Buchler, Jared Stokes, James Kragel, and Roberto Cabeza. "Neural Correlates of Confidence during Item Recognition and Source Memory Retrieval: Evidence for Both Dual-process and Strength Memory Theories." Journal of Cognitive Neuroscience 23, no. 12 (December 2011): 3959–71. http://dx.doi.org/10.1162/jocn_a_00086.
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Although the medial-temporal lobes (MTL), PFC, and parietal cortex are considered primary nodes in the episodic memory network, there is much debate regarding the contributions of MTL, PFC, and parietal subregions to recollection versus familiarity (dual-process theory) and the feasibility of accounts on the basis of a single memory strength process (strength theory). To investigate these issues, the current fMRI study measured activity during retrieval of memories that differed quantitatively in terms of strength (high vs. low-confidence trials) and qualitatively in terms of recollection versus familiarity (source vs. item memory tasks). Support for each theory varied depending on which node of the episodic memory network was considered. Results from MTL best fit a dual-process account, as a dissociation was found between a right hippocampal region showing high-confidence activity during the source memory task and bilateral rhinal regions showing high-confidence activity during the item memory task. Within PFC, several left-lateralized regions showed greater activity for source than item memory, consistent with recollective orienting, whereas a right-lateralized ventrolateral area showed low-confidence activity in both tasks, consistent with monitoring processes. Parietal findings were generally consistent with strength theory, with dorsal areas showing low-confidence activity and ventral areas showing high-confidence activity in both tasks. This dissociation fits with an attentional account of parietal functions during episodic retrieval. The results suggest that both dual-process and strength theories are partly correct, highlighting the need for an integrated model that links to more general cognitive theories to account for observed neural activity during episodic memory retrieval.
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Karimi, Mostafa, Arman Hasanzadeh, and Yang Shen. "Network-principled deep generative models for designing drug combinations as graph sets." Bioinformatics 36, Supplement_1 (July 1, 2020): i445—i454. http://dx.doi.org/10.1093/bioinformatics/btaa317.
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Abstract Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information Supplementary data are available at Bioinformatics online.
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Tosh, Colin R., Graeme D. Ruxton, Jens Krause, and Daniel W. Franks. "Experiments with humans indicate that decision accuracy drives the evolution of niche width." Proceedings of the Royal Society B: Biological Sciences 278, no. 1724 (April 13, 2011): 3504–9. http://dx.doi.org/10.1098/rspb.2011.0478.
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One theory to explain the high incidence of niche specialization in many animals is that it reduces attentional load during resource-seeking behaviour and thus leads to more accurate resource selection. A recent neural network model refined the predictions of this theory, indicating that a cognitive advantage in specialists is likely to occur under realistic ecological conditions, namely when ‘mistakes’ (i.e. selection of non-host resources) contribute moderately but positively to fitness. Here, we present a formal empirical test of the predictions of this model. Using a human–computer interactive, we demonstrate that the central prediction of the model is supported: specialist humans are more accurate decision-makers than generalists when their mistakes are rewarded, but not when mistakes are punished. The idea that increased decision accuracy drives the evolution of niche width in animals has been supported in almost all empirical systems in which it has been investigated. Theoretical work supports the idea, and now the predictions of a key theoretical model have been demonstrated in a real biological information-processing system. Considering these interlocking pieces of evidence, we argue that specialization through increased decision accuracy may contribute significantly, along with other mechanisms, to promote niche specialization in animals.
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Troche, Stefan J., Helene M. von Gugelberg, Olivier Pahud, and Thomas H. Rammsayer. "Do Executive Attentional Processes Uniquely or Commonly Explain Psychometric g and Correlations in the Positive Manifold? A Structural Equation Modeling and Network-Analysis Approach to Investigate the Process Overlap Theory." Journal of Intelligence 9, no. 3 (July 15, 2021): 37. http://dx.doi.org/10.3390/jintelligence9030037.
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One of the best-established findings in intelligence research is the pattern of positive correlations among various intelligence tests. Although this so-called positive manifold became the conceptual foundation of many theoretical accounts of intelligence, the very nature of it has remained unclear. Only recently, Process Overlap Theory (POT) proposed that the positive manifold originated from overlapping domain-general, executive processes. To test this assumption, the functional relationship between different aspects of executive attention and the positive manifold was investigated by re-analyzing an existing dataset (N = 228). Psychometric reasoning, speed, and memory performance were assessed by a short form of the Berlin Intelligence Structure test. Two aspects of executive attention (sustained and selective attention) and speed of decision making were measured by a continuous performance test, a flanker task, and a Hick task, respectively. Traditional structural equation modeling, representing the positive manifold by a g factor, as well as network analyses, investigating the differential effects of the two aspects of executive attention and speed of decision making on the specific correlations of the positive manifold, suggested that selective attention, sustained attention, and speed of decision making explained the common but not the unique portions of the positive manifold. Thus, we failed to provide evidence for POT’s assumption that the positive manifold is the result of overlapping domain-general processes. This does not mean that domain-general processes other than those investigated here will not be able to show the pattern of results predicted by POT.
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Li, Qiang. "Question and Answer Techniques for Financial Audits in Universities Based on Deep Learning." Mathematical Problems in Engineering 2022 (May 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/4875859.
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Financial auditing in universities is highly specialized, with a huge knowledge system and rapid updates. Auditors will encounter various problems and situations in their work and need to acquire domain knowledge efficiently and accurately to solve the difficulties they encounter. The existing audit information software, however, is mostly aimed at the management of audit affairs and lacks the relevant functions to acquire and retrieve knowledge of specific audit domains. In this study, we use deep learning theory as support to conduct an in-depth study on the key technologies of question and answer systems in the field of financial auditing in universities. In the question-answer retrieval stage, the local information and the global information of the sentence are first modelled using a two-way coding model based on the attentional mechanism, and then, an interactive text matching model is used to interact directly at the input layer, and a multilayer convolutional neural network model cable news network (CNN) is used to extract the fine-grained matching features from the interaction matrix; this study adopts two matching methods. We have conducted comparative experiments to verify the effectiveness and application value of the entity recognition algorithm based on this study’s algorithm and the question-answer retrieval model based on multi-granularity text matching in the university financial audit domain.
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Weidner, R., N. J. Shah, and G. R. Fink. "The Neural Basis of Perceptual Hypothesis Generation and Testing." Journal of Cognitive Neuroscience 18, no. 2 (February 1, 2006): 258–66. http://dx.doi.org/10.1162/jocn.2006.18.2.258.
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Four-dot masking is a new form of visual masking that does not involve local contour interactions or spatial superimposition of the target stimulus and the mask (as, e.g., in pattern or metacontrast masking). Rather, the effective masking mechanism is based on object substitution. Object substitution masking occurs when low-level visual information representations are altered before target identification through iterative interaction with high-level visual processing stages has been completed. Interestingly, object substitution interacts with attention processes: Strong masking effects are observed when attentional orientation toward the target location is delayed. In contrast, no masking occurs when attention can be rapidly shifted to and engaged onto the target location. We investigated the neural basis of object substitution masking by studying the interaction of spatial attention and masking processes using functional magnetic resonance imaging. Behavioral data indicated a two-way interaction between the factors Spatial Attention (valid vs. invalid cueing) and Masking (four-dot vs. pattern masking). As expected, spatial attention improved performance more strongly during object substitution masking. Functional correlates of this interaction were found in the primary visual cortex, higher visual areas, and left intraparietal sulcus. A region-of-interest analysis in these areas revealed that the largest blood oxygenation level-dependent signal changes occurred during effective four-dot masking. In contrast, the weakest signal changes in these areas were observed when target visibility was highest. The data suggest that these areas represent an object substitution network dedicated to the generation and testing of a perceptual hypotheses as described by the object substitution theory of masking of Di-Lollo et al. [Competition for consciousness among visual events: The psychophysics of reentrant visual processes. Journal of Experimental Psychology: General, 129, 481–507, 2000].
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Garland, Eric L., Adam W. Hanley, Anne K. Baker, and Matthew O. Howard. "Biobehavioral Mechanisms of Mindfulness as a Treatment for Chronic Stress: An RDoC Perspective." Chronic Stress 1 (February 2017): 247054701771191. http://dx.doi.org/10.1177/2470547017711912.
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Mindfulness-based interventions have been heralded as promising means of alleviating chronic stress. While meta-analyses indicate that mindfulness-based interventions significantly reduce global measures of stress, how mindfulness-based interventions modulate the specific mechanisms underpinning chronic stress as operationalized by the National Institute of Mental Health research domain criteria (RDoC) of sustained threat has not yet been detailed in the literature. To address this knowledge gap, this article aims to (1) review evidence that mindfulness-based interventions ameliorate each of the 10 elements of behavioral dysregulation characterizing sustained threat via an array of mindful counter-regulatory strategies; (2) review evidence that mindfulness-based interventions modify biological domains implicated in sustained threat, such as the hypothalamic–pituitary–adrenal axis, as well as brain circuits involved in attentional function, limbic reactivity, habit behavior, and the default mode network; and (3) integrate these findings into a novel conceptual framework of mindful self-regulation in the face of stress—the Mindfulness-to-Meaning Theory. Taken together, the extant body of scientific evidence suggests that the practice of mindfulness enhances a range biobehavioral factors implicated in adaptive stress coping and induces self-referential plasticity, leading to the ability to find meaning in adversity. These mechanistic findings can inform the treatment development process to optimize the next generation of mindfulness-based interventions for greater therapeutic efficacy.
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Rainville, Pierre, Robert K. Hofbauer, M. Catherine Bushnell, Gary H. Duncan, and Donald D. Price. "Hypnosis Modulates Activity in Brain Structures Involved in the Regulation of Consciousness." Journal of Cognitive Neuroscience 14, no. 6 (August 1, 2002): 887–901. http://dx.doi.org/10.1162/089892902760191117.
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The notion of consciousness is at the core of an ongoing debate on the existence and nature of hypnotic states. Previously, we have described changes in brain activity associated with hypnosis (Rainville, Hofbauer, Paus, Duncan, Bushnell, & Price, 1999). Here, we replicate and extend those findings using positron emission tomography (PET) in 10 normal volunteers. Immediately after each of 8 PET scans performed before (4 scans) and after (4 scans) the induction of hypnosis, subjects rated their perceived level of “mental relaxation” and “mental absorption,” two of the key dimensions describing the experience of being hypnotized. Regression analyses between regional cerebral blood flow (rCBF) and self-ratings confirm the hypothesized involvement of the anterior cingulate cortex (ACC), the thalamus, and the ponto-mesencephalic brainstem in the production of hypnotic states. Hypnotic relaxation further involved an increase in occipital rCBF that is consistent with our previous interpretation that hypnotic states are characterized by a decrease in cortical arousal and a reduction in cross-modality suppression (disinhibition). In contrast, increases in mental absorption during hypnosis were associated with rCBF increases in a distributed network of cortical and subcortical structures previously described as the brain's attentional system. These findings are discussed in support of a state theory of hypnosis in which the basic changes in phenomenal experience produced by hypnotic induction reflect, at least in part, the modulation of activity within brain areas critically involved in the regulation of consciousness.
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Chen, Yuhan, Takashi Matsubara, and Takaharu Yaguchi. "KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6322–32. http://dx.doi.org/10.1609/aaai.v36i6.20582.
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Many physical phenomena are described by Hamiltonian mechanics using an energy function (Hamiltonian). Recently, the Hamiltonian neural network, which approximates the Hamiltonian by a neural network, and its extensions have attracted much attention. This is a very powerful method, but theoretical studies are limited. In this study, by combining the statistical learning theory and KAM theory, we provide a theoretical analysis of the behavior of Hamiltonian neural networks when the learning error is not completely zero. A Hamiltonian neural network with non-zero errors can be considered as a perturbation from the true dynamics, and the perturbation theory of the Hamilton equation is widely known as KAM theory. To apply KAM theory, we provide a generalization error bound for Hamiltonian neural networks by deriving an estimate of the covering number of the gradient of the multi-layer perceptron, which is the key ingredient of the model. This error bound gives a sup-norm bound on the Hamiltonian that is required in the application of KAM theory.
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Zheng, Zibin, Angyu Zheng, Liang Chen, Yangjun Xu, and Fenfang Xie. "Service recommendation through graph attention network in heterogeneous information networks." International Journal of Computational Science and Engineering 25, no. 6 (2022): 643. http://dx.doi.org/10.1504/ijcse.2022.10052326.
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Xie, Fenfang, Yangjun Xu, Angyu Zheng, Liang Chen, and Zibin Zheng. "Service recommendation through graph attention network in heterogeneous information networks." International Journal of Computational Science and Engineering 25, no. 6 (2022): 643. http://dx.doi.org/10.1504/ijcse.2022.127186.
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Spreng, R. Nathan, Jorge Sepulcre, Gary R. Turner, W. Dale Stevens, and Daniel L. Schacter. "Intrinsic Architecture Underlying the Relations among the Default, Dorsal Attention, and Frontoparietal Control Networks of the Human Brain." Journal of Cognitive Neuroscience 25, no. 1 (January 2013): 74–86. http://dx.doi.org/10.1162/jocn_a_00281.
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Human cognition is increasingly characterized as an emergent property of interactions among distributed, functionally specialized brain networks. We recently demonstrated that the antagonistic “default” and “dorsal attention” networks—subserving internally and externally directed cognition, respectively—are modulated by a third “frontoparietal control” network that flexibly couples with either network depending on task domain. However, little is known about the intrinsic functional architecture underlying this relationship. We used graph theory to analyze network properties of intrinsic functional connectivity within and between these three large-scale networks. Task-based activation from three independent studies were used to identify reliable brain regions (“nodes”) of each network. We then examined pairwise connections (“edges”) between nodes, as defined by resting-state functional connectivity MRI. Importantly, we used a novel bootstrap resampling procedure to determine the reliability of graph edges. Furthermore, we examined both full and partial correlations. As predicted, there was a higher degree of integration within each network than between networks. Critically, whereas the default and dorsal attention networks shared little positive connectivity with one another, the frontoparietal control network showed a high degree of between-network interconnectivity with each of these networks. Furthermore, we identified nodes within the frontoparietal control network of three different types—default-aligned, dorsal attention-aligned, and dual-aligned—that we propose play dissociable roles in mediating internetwork communication. The results provide evidence consistent with the idea that the frontoparietal control network plays a pivotal gate-keeping role in goal-directed cognition, mediating the dynamic balance between default and dorsal attention networks.
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Guo, Rongchun. "News Hotspot Event Diffusion Mechanism Based on Complex Network." Mathematical Problems in Engineering 2022 (May 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/1455324.
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The wide range of social hot news events on the Internet has made the Internet have a great impact on the public. However, there are few studies on Internet information. In order to improve the efficiency of user network information dissemination of Internet information based on complex network theory and model simulation, this paper makes a more in-depth study on information dissemination on the Internet, constructs a complex network of Internet information dissemination, and analyzes the static topology and dynamic evolution process of the network. Using the attention relationship between Internet users, the Internet information dissemination network, degree, and path were used to select multiple indicators. The static topology of the network is analyzed by using the complex network theory. The study found that the complex network of Internet information is different from other complex networks. The influencing factors of network dynamic evolution are studied from three aspects: overall structure, local structure, and time constraints. The evolution trend of different forms and overall network nodes in the evolution process was explored, and the network dynamic evolution process model was constructed. Practice shows that the model can better describe the evolution process of network information dissemination in complex networks. The degree values of the two networks are positively correlated with the corresponding average clustering coefficients, and the networks have a significant hierarchy. The correlation between news hot events and network nodes is not as good as users’ attention to the network.
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Russo, Paul, and Oded Nov. "Photo Tagging Over Time: A Longitudinal Study of the Role of Attention, Network Density, and Motivations." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (May 16, 2010): 146–53. http://dx.doi.org/10.1609/icwsm.v4i1.14022.
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Along with the growth in artifact sharing in online communities such as Flickr, YouTube, and Facebook comes the demand for adding descriptive meta-information, or tags. Tags help individuals to organize and communicate the content and context of their work for themselves and for others. This longitudinal study draws on research in social psychology, network theory and online communities to explain tagging over time. Our findings suggest that tagging increases as a contributor receives attention from others in the community. Further, we find that the more a user's network neighbors are connected to each other directly, the less the focal user will tend to tag his photos. However, density interacts with attention such that those who are surrounded by a dense ego network respond more to attention than others whose ego networks are sparsely interconnected. Unexpectedly, we find no direct correlation between tagging and the individual motivations of enjoyment and commitment. While commitment is not directly associated with tagging, there is an interaction effect such that the effect of commitment on tagging is positive for users with low-density ego networks and negative when a user is surrounded by a high-density network. Directions for future research as well as implications for theory and practice are discussed.
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Xiang, Zhijie, Weijia Gong, Zehui Li, Xue Yang, Jihua Wang, and Hong Wang. "Predicting Protein–Protein Interactions via Gated Graph Attention Signed Network." Biomolecules 11, no. 6 (May 28, 2021): 799. http://dx.doi.org/10.3390/biom11060799.
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Protein–protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning. In practice, PPIs can be expressed as a signed network (i.e., graph structure), wherein the nodes in the network represent proteins, and edges represent the interactions (positive or negative effects) of protein nodes. PPI predictions can be realized by predicting the links of the signed network; therefore, the use of gated graph attention for signed networks (SN-GGAT) is proposed herein. First, the concept of graph attention network (GAT) is applied to signed networks, in which “attention” represents the weight of neighbor nodes, and GAT updates the node features through the weighted aggregation of neighbor nodes. Then, the gating mechanism is defined and combined with the balance theory to obtain the high-order relations of protein nodes to improve the attention effect, making the attention mechanism follow the principle of “low-order high attention, high-order low attention, different signs opposite”. PPIs are subsequently predicted on the Saccharomyces cerevisiae core dataset and the Human dataset. The test results demonstrate that the proposed method exhibits strong competitiveness.
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Gao, Jianxi, Daqing Li, and Shlomo Havlin. "From a single network to a network of networks." National Science Review 1, no. 3 (July 16, 2014): 346–56. http://dx.doi.org/10.1093/nsr/nwu020.
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Abstract Network science has attracted much attention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-profile publications, and it seems that since 2010 studies of ‘network of networks’ (NON), sometimes called multilayer networks or multiplex, have attracted more and more attention. The analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensively in physics and mathematics in the last 50 years is a specific limit of the rich and very different general case of n coupled networks. Since then, properties and dynamics of interdependent and interconnected networks have been studied extensively, and scientists are finding many interesting results and discovering many surprising phenomena. Because most natural and engineered systems are composed of multiple subsystems and layers of connectivity, it is important to consider these features in order to improve our understanding of such complex systems. Now the study of NON has become one of the important directions in network science. In this paper, we review recent studies on the new emerging area—NON. Due to the fast growth of this field, there are many definitions of different types of NON, such as interdependent networks, interconnected networks, multilayered networks, multiplex networks and many others. There exist many datasets that can be represented as NON, such as network of different transportation networks including flight networks, railway networks and road networks, network of ecological networks including species interacting networks and food webs, network of biological networks including gene regulation network, metabolic network and protein–protein interacting network, network of social networks and so on. Among them, many interdependent networks including critical infrastructures are embedded in space, introducing spatial constraints. Thus, we also review the progress on study of spatially embedded networks. As a result of spatial constraints, such interdependent networks exhibit extreme vulnerabilities compared with their non-embedded counterparts. Such studies help us to understand, realize and hopefully mitigate the increasing risk in NON.
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Lobastova, M., A. Matyukhin, and A. Muthanna. "Analysis of Network Reliability of Network Synchronization." Telecom IT 8, no. 4 (December 23, 2020): 93–99. http://dx.doi.org/10.31854/2307-1303-2020-8-4-93-99.
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This article describes the challenges of modern communication networks reliability, analyses ITU-T recommendations and regulations governing the communication networks reliability in Russian Federation. The network clock network is an integral part of digital communication networks. Therefore, the issue of the synchronization network reliability should be given great attention. Research subject. In this article, we discussed the reliability of the clock synchronization network. Method. The main mathematical tools are graph theory and probability theory. To implement the proposed method for assessing the structural reliability of the synchronization network, the direct search method is used. Core results. The results allow us to conclude that the proposed method can be applied to assess the structural reliability of the clock synchronization network. Practical relevance. The solution proposed in this article can be used for a reasonable assessment of the network structural reliability indicators, which is necessary for making a decision on the choice of a route for transmitting a synchronization signal.
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Huang, Junjie, Huawei Shen, Liang Hou, and Xueqi Cheng. "SDGNN: Learning Node Representation for Signed Directed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 196–203. http://dx.doi.org/10.1609/aaai.v35i1.16093.
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Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies on real-world datasets to analyze the social mechanism in signed directed networks. Guided by related socio- logical theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks. The proposed model simultaneously reconstructs link signs, link directions, and signed directed triangles. We validate our model’s effectiveness on five real-world datasets, which are commonly used as the benchmark for signed network embeddings. Experiments demonstrate the proposed model outperforms existing models, including feature-based methods, network embedding methods, and several GNN methods.
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Fried, Eiko I., and Angélique O. J. Cramer. "Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology." Perspectives on Psychological Science 12, no. 6 (September 5, 2017): 999–1020. http://dx.doi.org/10.1177/1745691617705892.
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Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.
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Chechlacz, M., C. R. Gillebert, S. A. Vangkilde, A. Petersen, and G. W. Humphreys. "Structural Variability within Frontoparietal Networks and Individual Differences in Attentional Functions: An Approach Using the Theory of Visual Attention." Journal of Neuroscience 35, no. 30 (July 29, 2015): 10647–58. http://dx.doi.org/10.1523/jneurosci.0210-15.2015.
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Huang, Libingyi, Guoqing Jia, Weidong Fang, Wei Chen, and Wuxiong Zhang. "Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures." Sensors 20, no. 1 (December 30, 2019): 221. http://dx.doi.org/10.3390/s20010221.
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The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility.
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Dimitrova, Zlatinka I. "Flows of Substances in Networks and Network Channels: Selected Results and Applications." Entropy 24, no. 10 (October 18, 2022): 1485. http://dx.doi.org/10.3390/e24101485.
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This review paper is devoted to a brief overview of results and models concerning flows in networks and channels of networks. First of all, we conduct a survey of the literature in several areas of research connected to these flows. Then, we mention certain basic mathematical models of flows in networks that are based on differential equations. We give special attention to several models for flows of substances in channels of networks. For stationary cases of these flows, we present probability distributions connected to the substance in the nodes of the channel for two basic models: the model of a channel with many arms modeled by differential equations and the model of a simple channel with flows of substances modeled by difference equations. The probability distributions obtained contain as specific cases any probability distribution of a discrete random variable that takes values of 0,1,…. We also mention applications of the considered models, such as applications for modeling migration flows. Special attention is given to the connection of the theory of stationary flows in channels of networks and the theory of the growth of random networks.
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Stevanović, Kosara. "Social ties between criminal networks in cocaine trafficking in Europe." Crimen 11, no. 3 (2020): 325–45. http://dx.doi.org/10.5937/crimen2003325s.
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This paper is highlighting the main criminal networks that are trafficking cocaine in Europe, through the lenses of social embeddedness and criminal network theories. We will try to show that social ties between European and Latin American organized crime networks, as well as between different European crime networks, are the main reason for the staggering success of European criminal groups in cocaine trafficking in the 21st century. In the beginning, we lay out the social embeddedness theory and criminal network theory, and then we review the main criminal networks involved in cocaine trafficking in Europe and social ties between them, with special attention to Serbian and Montenegrin criminal networks. At the end of the article, we analyze what role does ethnicity, seen as social ties based on common language and tradition, play in cocaine trafficking in Europe.
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Wilkinson, Michael. "Charismatic Christianity and the Role of Social Networks." PNEUMA 38, no. 1-2 (2016): 33–49. http://dx.doi.org/10.1163/15700747-03801005.
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This article offers a sociological examination of the role of networks among charismatic Christians, with specific attention to Catch the Fire and the Revival Alliance. Drawing upon social network theory, it shows how religious networks in global society are relational, asymmetrical, and infused with apostolic authority. A case study of Catch the Fire reveals that the network is primarily collaborative in its structure. However, there are some relationships in the network that are more important than others, as evidenced by the dense social ties among members. Furthermore, the network is structured according to gender with the benefits of social capital favoring men. The network also overlaps with other networks through key relationships, especially the New Apostolic Reformation and other charismatic ministries associated with the prosperity gospel.
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Shang, Jun, Hao Qiang Liu, Qiang Liu, and Zi Qi Liu. "Design of the Small World Model by NS2." Applied Mechanics and Materials 496-500 (January 2014): 2338–41. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2338.
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WSN is the network which is used mostly in the world nowadays, and it has the characteristics that lower cost and better functions than other kinds of the network, and the WSN network is built by the ordinary nodes and the super nodes.Theoretical study of the complex network is widely involved in the fields of computer networks, and the applied research becomes more and more important in the future. It has caused many academic attention about how to apply the complex network theory among the specific application in recent years. In the complex network theory, there has been a number of important research results about the use of the small-world network, scale-free network in the field of transportation.
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Zheng, Jing, Ziren Gao, Jingsong Ma, Jie Shen, and Kang Zhang. "Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection." ISPRS International Journal of Geo-Information 10, no. 11 (November 11, 2021): 768. http://dx.doi.org/10.3390/ijgi10110768.
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The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs or strokes, have low automaticity and are highly subjective. Graph convolutional networks (GCNs) combine graph theory with neural networks; thus, they can not only extract spatial information but also realize automatic selection. Therefore, in this study, we adopted GCNs for automatic road network selection and transformed the process into one of node classification. In addition, to solve the problem of gradient vanishing in GCNs, we compared and analyzed the results of various GCNs (GraphSAGE and graph attention networks [GAT]) by selecting small-scale road networks under different deep architectures (JK-Nets, ResNet, and DenseNet). Our results indicate that GAT provides better selection of road networks than other models. Additionally, the three abovementioned deep architectures can effectively improve the selection effect of models; JK-Nets demonstrated more improvement with higher accuracy (88.12%) than other methods. Thus, our study shows that GCN is an appropriate tool for road network selection; its application in cartography must be further explored.
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Friedkin, Noah E., Anton V. Proskurnikov, and Francesco Bullo. "Positive contagion and the macrostructures of generalized balance." Network Science 7, no. 4 (September 20, 2019): 445–58. http://dx.doi.org/10.1017/nws.2019.19.
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AbstractBalance theory has advanced with interdisciplinary contributions from social science, physical science, engineering, and mathematics. The common focus of attention is social networks in which every individual has either a positive or negative, cognitive or emotional, appraisal of every other individual. The current frontier of work on balance theory is a hunt for a dynamical model that predicts the temporal evolution of any such appraisal network to a particular structure in the complete set of balanced networks allowed by the theory. Finding such a model has proved to be a difficult problem. In this article, we contribute a parsimonious solution of the problem that explicates the conditions under which a network will evolve either to a set of mutually antagonistic cliques or to an asymmetric structure that allows agreement, cooperation, and compromise among cliques.
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Danon, Leon, Ashley P. Ford, Thomas House, Chris P. Jewell, Matt J. Keeling, Gareth O. Roberts, Joshua V. Ross, and Matthew C. Vernon. "Networks and the Epidemiology of Infectious Disease." Interdisciplinary Perspectives on Infectious Diseases 2011 (2011): 1–28. http://dx.doi.org/10.1155/2011/284909.
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The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.
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Boja, Nicolae. "Basic general concepts in the network analysis." Theoretical and Applied Mechanics 31, no. 3-4 (2004): 235–63. http://dx.doi.org/10.2298/tam0404235b.
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This survey is concerned oneself with the study of those types of material networks which can be met both in civil engineering and also in electrotechnics, in mechanics, or in hydrotechnics, and of which behavior lead to linear problems, solvable by means of Finite Element Method and adequate algorithms. Here, it is presented a unitary theory of networks met in the domains mentioned above and this one is illustrated with examples for the structural networks in civil engineering, electric circuits, and water supply networks, but also planar or spatial mechanisms can be comprised in this theory. The attention is focused to make evident the essential proper- ties and concepts in the network analysis, which differentiate the networks under force from other types of material networks. To such a network a planar, connected, and directed or undirected graph is associated, and with some vector fields on the vertex set this graph is endowed. .
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Xu, Xin-Jian, Hong-Xiang Gao, Liu-Cun Zhu, and Rui Zhu. "Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs." Life 13, no. 1 (December 27, 2022): 76. http://dx.doi.org/10.3390/life13010076.
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Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.
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Mitterlechner, Matthias. "Governing integrated care networks through collaborative inquiry." Journal of Health Organization and Management 32, no. 7 (October 8, 2018): 860–74. http://dx.doi.org/10.1108/jhom-01-2018-0012.
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Purpose The purpose of this paper is to develop a theory of governing in integrated care networks. Asking how and why the governance of these networks emerges and evolves over time, it responds to calls for more innovative thinking in this field. Design/methodology/approach Data result from a rare longitudinal qualitative case study conducted with the Healthcare Centre Lower Engadin, the lead organisation of pioneering health and social care network in a rural Swiss region. Findings Actors governed the network through repetitive sequences of collaborative inquiry, a practice through which they defined and addressed recurrent problems of network governance and joint network action in creative and experimental ways. Research limitations/implications Explaining how and why the governance of integrated care networks emerges and evolves, this study adds a dynamic theory to previous research, which has studied the determinants of effective network governance without considering their temporal evolution. It also contributes to the wider network literature, drawing attention to the pivotal role of meaning making, creativity and experimentation for understanding network governance dynamics. Practical implications The study invites practitioners to reflect on how they want to design collaborative inquiry in their own contexts. Important design levers include the creation of communication forums, trust and information transparency. Originality/value The study adds a rare longitudinal perspective on the governance of integrated care networks.
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Xu, Yingying, Liangqun Qi, Xichen Lyu, and Xinyu Zang. "An Evolution Analysis of Collaborative Innovation Network considering Government Subsidies and Supervision." Mathematical Problems in Engineering 2019 (July 29, 2019): 1–12. http://dx.doi.org/10.1155/2019/2906908.
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Collaborative innovation networks have the basic attributes of complex networks. The interaction of innovation network members has promoted the development of collaborative innovation networks. Using the game-based theory in the B-A scale-free network context, this paper builds an evolutionary game model of network members and explores the emergence mechanism from collaborative innovation behavior to the macroevolution of networks. The results show that revenue distribution, compensation of the betrayer, government subsidies, and supervision have positively contributed to the continued stability of collaborative innovation networks. However, the effect mechanisms are dissimilar for networks of different scales. In small networks, the rationality of the revenue distribution among members that have similar strengths should receive more attention, and the government should implement medium-intensity supervision measures. In large networks, however, compensation of the betrayer should be attached greater importance to, and financial support from the government can promote stable evolution more effectively.
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Song, Wenjing, Sanyang Liu, and Yiguang Bai. "Effective optimal dismantling strategy for interdependent networks based on residual theory." International Journal of Modern Physics C 30, no. 11 (November 2019): 1950082. http://dx.doi.org/10.1142/s0129183119500827.
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Because of interdependence between different network layers, interdependent networks are more fragile than single-layer networks, and large-scale iterative paralysis occurs easily. How to seek nodes whose removal can effectively dismantle networks has attracted great research attention. In this paper, a novel optimal dismantling strategy Maximum Entropy Centrality (EC) and overlapping betweenness (OB) based on residual theory (ECOB) is proposed. In the ECOB, the residual theory is used to detect the highest influence nodes according to the quality of the residual networks. In addition, to make sorting more accurate, EC and OB parameters are both considered in the node selection mechanism. Simulation shows that the ECOB strategy performs much better than existing methods both in artificial interdependent networks and real-world interdependent networks. This is thanks to the introduced ECOB node selection algorithm with proper parameter criterions.
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Zhang, Chengjun, Yi Lei, Xinyu Shen, Qi Li, Hui Yao, Di Cheng, Yifan Xie, and Wenbin Yu. "Fragility Induced by Interdependency of Complex Networks and Their Higher-Order Networks." Entropy 25, no. 1 (December 23, 2022): 22. http://dx.doi.org/10.3390/e25010022.
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The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order networks can explore hub structures in transportation networks and information dissemination units in neuronal networks. Therefore, the destruction of the connectivity of higher-order networks will cause significant damage to network functionalities. Meanwhile, previous works pointed out that the function of a complex network depends on the giant component of the original(low-order) network. Therefore, the network functionality will be influenced by both the low-order and its corresponding higher-order network. To study this issue, we build a network model of the interdependence of low-order and higher-order networks (we call it ILH). When some low-order network nodes fail, the low-order network’s giant component shrinks, leading to changes in the structure of the higher-order network, which further affects the low-order network. This process occurs iteratively; the propagation of the failure can lead to an eventual network crash. We conducted experiments on different networks based on the percolation theory, and our network percolation results demonstrated a first-order phase transition feature. In particular, we found that an ILH is more fragile than the low-order network alone, and an ILH is more likely to be corrupted in the event of a random node failure.
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Zhu, Heng Jun, and Rui Wang. "Analysis of Conflict Resolution Model in Campus WLAN Wireless Network." Advanced Materials Research 989-994 (July 2014): 4590–93. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4590.
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In the campus WLAN wireless networks, transfer speed of network data is attracted great attention of users. How to solve conflict problem occurred in the network data transmission process has become the main goal of the campus WLAN network optimization. This paper presents campus WLAN wireless networks conflict resolution based channel mapping anti-collision algorithm. According to the amount of data to be transmitted in the network, the data length is estimated to provide conflict resolution with support data. On the basis of the channel mapping anti-collision algorithm related theory, conflict resolution model is established. Experimental results show that the proposed algorithm used in resolving conflict in campus WLAN wireless network can reduce the probability of data conflict happened in channel, so as to improve the performance of campus WLAN wireless network.
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DU, FANG, QI XUAN, and TIE-JUN WU. "EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS." International Journal of Modern Physics C 21, no. 07 (July 2010): 955–71. http://dx.doi.org/10.1142/s0129183110015592.
Full textAbstract:
Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.