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Journal articles on the topic 'Network Dependency Analysis'
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Woldeyohannes, Yordanos T., and Yuming Jiang. "Measures for Network Structural Dependency Analysis." IEEE Communications Letters 22, no. 10 (2018): 2052–55. http://dx.doi.org/10.1109/lcomm.2018.2864109.
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Kudĕlka, Miloš, Šárka Zehnalová, Zdenĕk Horák, Pavel Krömer, and Václav Snášel. "Local dependency in networks." International Journal of Applied Mathematics and Computer Science 25, no. 2 (2015): 281–93. http://dx.doi.org/10.1515/amcs-2015-0022.
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Abstract Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.
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Duo, Jiecairang, Quecairang Hua, Keyou Huan, and Rangdangzhi Cai. "Transition based neural network dependency parsing of Tibetan." MATEC Web of Conferences 336 (2021): 06018. http://dx.doi.org/10.1051/matecconf/202133606018.
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In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic analysis of Tibetan language dependence. Part of the corpus of Qinghai Normal University’s part-of-speech tag set is marked by the corresponding mapping relationship is transformed into the corpus annotated by the national standard part-of-speech tag set. At the same time, the CoNLL format Tibetan language dependency syntax tree library is constructed, and the method of shift-reduce plus neural network is adopted to systematically study and analyze the Tibetan language dependency syntax. Thereby improving the quality of Tibetan dependency syntactic analysis, and its accuracy rate reaches UAS:94.59%
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Wu, Fei, and Xinfu Li. "Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis." Applied Sciences 13, no. 17 (2023): 9669. http://dx.doi.org/10.3390/app13179669.
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The task of aspect-based sentiment analysis (ABSA) is to detect the sentiment polarity toward given aspects. Contemporary methods predominantly utilize graph neural networks and incorporate attention mechanisms to dynamically connect aspect terms with their surrounding contexts, resulting in more informative feature representations. However, these methods only consider whether there are dependencies between words when introducing dependencies, ignoring that dependencies between different sentiment words have different effects. Neglecting this could introduce noise and negatively impact the model’s performance. To overcome this limitation, we introduce a novel approach called the local dependency-enhanced graph convolutional network (LDEGCN). Our method combines semantic information and dependency relationships to better capture the affective relationships between words. Specifically, we integrate sentiment knowledge from SenticNet to enrich the sentence’s dependency graph and thoroughly explore the dependency types between contexts and aspects to focus on particular dependency types. The local context weight (LCW) method is employed on the dependency-enhanced graph to emphasize the importance of local contexts, thereby mitigating the issue of long-distance dependencies. Through extensive evaluations of five public datasets, the LDEGCN model demonstrates significant improvements over mainstream models.
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Ruslan, Mohd Firdaus. "Proposing A Conceptual Model for Asnafpreneur Success: Social Networks and Resource Dependence in Low-Income Regions." International Journal of Research and Innovation in Social Science VIII, no. X (2024): 935–41. http://dx.doi.org/10.47772/ijriss.2024.8100076.
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This paper proposes a conceptual model integrating Social Network Theory (SNT) and Resource Dependence Theory (RDT) to examine the business performance of Asnafpreneurs—entrepreneurs from marginalized communities supported by zakat—in low-income regions. While Asnafpreneurs frequently rely on social networks to access resources, they face significant challenges, including limited network diversity and heavy dependency on external actors like zakat institutions for financial and operational support. The model presented in this study aims to demonstrate how optimizing social networks can help reduce resource dependency and improve business outcomes, such as revenue growth, sustainability, and entrepreneurial autonomy. To guide future empirical validation, the paper suggests a mixed-methods approach, combining qualitative insights from in-depth interviews with quantitative measures, including social network analysis and structural equation modeling, to test the proposed relationships. This approach provides a roadmap for assessing the impact of social networks on business performance and understanding the mediating role of resource dependency. The findings contribute to the theoretical development of SNT and RDT by extending their application to marginalized entrepreneurship within a faith-based context. The paper also offers practical implications for policymakers and zakat institutions, providing strategies for designing more effective entrepreneurial support programs aimed at empowering Asnafpreneurs in low-income communities.
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Musmeci, Nicolò, Vincenzo Nicosia, Tomaso Aste, Tiziana Di Matteo, and Vito Latora. "The Multiplex Dependency Structure of Financial Markets." Complexity 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9586064.
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We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex datasets. In particular, we consider multiplex networks made of four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. The study of the time evolution of the multiplex constructed from financial data uncovers important changes in intrinsically multiplex properties of the network, and such changes are associated with periods of financial stress. We observe that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks corresponding to each dependency measure.
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KENETT, DROR Y., TOBIAS PREIS, GITIT GUR-GERSHGOREN, and ESHEL BEN-JACOB. "DEPENDENCY NETWORK AND NODE INFLUENCE: APPLICATION TO THE STUDY OF FINANCIAL MARKETS." International Journal of Bifurcation and Chaos 22, no. 07 (2012): 1250181. http://dx.doi.org/10.1142/s0218127412501817.
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Much effort has been devoted to assess the importance of nodes in complex networks. Examples of commonly used measures of node importance include node degree, node centrality and node vulnerability score (the effect of the node deletion on the network efficiency). Here we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node–node correlations. The dependency network approach provides a new system level analysis of the activity and topology of directed networks. The approach extracts topological relations between the networks nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). The resulting dependency networks are a new class of correlation-based networks, and are capable of uncovering hidden information on the structure of the network. Here, we present a review of the new approach, and an example of its application to financial markets. We apply the methodology to the daily closing prices of all Dow Jones Industrial Average (DJIA) index components for the period 1939–2010. Investigating the structure and dynamics of the dependency network across time, we find fingerprints of past financial crises, illustrating the importance of this methodology.
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Yang, Jinjie, Anan Dai, Yun Xue, Biqing Zeng, and Xuejie Liu. "Syntactically Enhanced Dependency-POS Weighted Graph Convolutional Network for Aspect-Based Sentiment Analysis." Mathematics 10, no. 18 (2022): 3353. http://dx.doi.org/10.3390/math10183353.
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Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them merely considered if there exist dependencies between words, ignoring the types of these dependencies, which carry important information, as dependencies with different types have different effects. In addition, they neglected the correlations between dependency types and part-of-speech (POS) labels, which are helpful for utilizing dependency imformation. To address such limitations and the deficiency of insufficient syntactic and semantic feature mining, we propose a novel model containing three modules, which aims to leverage dependency trees more reasonably by distinguishing different dependencies and extracting beneficial syntactic and semantic features to further enhance model performance. To enrich word embeddings, we design a syntactic feature encoder (SynFE). In particular, we design Dependency-POS Weighted Graph Convolutional Network (DPGCN) to weight different dependencies by a graph attention mechanism we proposed. Additionally, to capture aspect-oriented semantic information, we design a semantic feature extractor (SemFE). Extensive experiments on five popular benchmark databases validate that our model can better employ dependency information and effectively extract favorable syntactic and semantic features to achieve new state-of-the-art performance.
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Hieu, Nong Minh, Antoine Ledent, Yunwen Lei, and Cheng Yeaw Ku. "Generalization Analysis for Deep Contrastive Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17186–94. https://doi.org/10.1609/aaai.v39i16.33889.
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In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as representation functions. We approach this problem from two angles. On the one hand, we derive a parameter-counting bound that scales with the overall size of the neural networks. On the other hand, we provide a norm-based bound that scales with the norms of neural networks' weight matrices. Ignoring logarithmic factors, the bounds are independent of the size of the tuples provided for contrastive learning. To the best of our knowledge, this property is only shared by one other work, which employed a different proof strategy and suffers from very strong exponential dependence on the depth of the network which is due to a use of the peeling technique. Our results circumvent this by leveraging powerful results on covering numbers with respect to uniform norms over samples. In addition, we utilize loss augmentation techniques to further reduce the dependency on matrix norms and the implicit dependence on network depth. In fact, our techniques allow us to produce many bounds for the contrastive learning setting with similar architectural dependencies as in the study of the sample complexity of ordinary loss functions, thereby bridging the gap between the learning theories of contrastive learning and DNNs.
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Jiang, Tingyao, Zilong Wang, Ming Yang, and Cheng Li. "Aspect-Based Sentiment Analysis with Dependency Relation Weighted Graph Attention." Information 14, no. 3 (2023): 185. http://dx.doi.org/10.3390/info14030185.
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Aspect-based sentiment analysis is a fine-grained sentiment analysis that focuses on the sentiment polarity of different aspects of text, and most current research methods use a combination of dependent syntactic analysis and graphical neural networks. In this paper, a graph attention network aspect-based sentiment analysis model based on the weighting of dependencies (WGAT) is designed to address the problem in that traditional models do not sufficiently analyse the types of syntactic dependencies; in the proposed model, graph attention networks can be weighted and averaged according to the importance of different nodes when aggregating information. The model first transforms the input text into a low-dimensional word vector through pretraining, while generating a dependency syntax graph by analysing the dependency syntax of the input text and constructing a dependency weighted adjacency matrix according to the importance of different dependencies in the graph. The word vector and the dependency weighted adjacency matrix are then fed into a graph attention network for feature extraction, and sentiment polarity is predicted through the classification layer. The model can focus on syntactic dependencies that are more important for sentiment classification during training, and the results of the comparison experiments on the Semeval-2014 laptop and restaurant datasets and the ACL-14 Twitter social comment dataset show that the WGAT model has significantly improved accuracy and F1 values compared to other baseline models, validating its effectiveness in aspect-level sentiment analysis tasks.
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SABRIN, KAESER M., and CONSTANTINE DOVROLIS. "The hourglass effect in hierarchical dependency networks." Network Science 5, no. 4 (2017): 490–528. http://dx.doi.org/10.1017/nws.2017.22.
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AbstractMany hierarchically modular systems are structured in a way that resembles an hourglass. This “hourglass effect” means that the system generates many outputs from many inputs through a relatively small number of intermediate modules that are critical for the operation of the entire system, referred to as the waist of the hourglass. We investigate the hourglass effect in general, not necessarily layered, hierarchical dependency networks. Our analysis focuses on the number of source-to-target dependency paths that traverse each vertex, and it identifies the core of a dependency network as the smallest set of vertices that collectively cover almost all dependency paths. We then examine if a given network exhibits the hourglass property or not, comparing its core size with a “flat” (i.e., non-hierarchical) network that preserves the source dependencies of each target in the original network. As a possible explanation for the hourglass effect, we propose the Reuse Preference model that captures the bias of new modules to reuse intermediate modules of similar complexity instead of connecting directly to sources or low complexity modules. We have applied the proposed framework in a diverse set of dependency networks from technological, natural, and information systems, showing that all these networks exhibit the general hourglass property but to a varying degree and with different waist characteristics.
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Hryschuk, Iryna, Andrii Astrakhantsev, Stanislav Pedan, and Larysa Globa. "ANALYSIS OF ROUTING PROTOCOLS CHARACTERISTICS IN AD-HOC NETWORK." Information and Telecommunication Sciences, no. 1 (June 28, 2024): 12–17. http://dx.doi.org/10.20535/2411-2976.12024.12-17.
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Background. Wireless ad-hoc networks are becoming increasingly prevalence in remote areas, in extreme environments, even in military operations, and in scenarios where setting up infrastructure networks is not possible. Research of ad-hoc routing protocols problems allows improving the efficiency of their operation in conditions of high variability in packet loss or instability of network operation when the speed of users changes. Objective. The purpose of the paper is analysis of packet loss dependency from a network operation time, study of a user speed influence on a network efficiency, and research of network operation efficiency with different routing protocols. Methods. The method of routing protocols efficiency evaluation is the simulation of their operation in an ad-hoc network on a test data set and research of a network indicators dependency in time under different loads and changing mobility of users. Results. The conducted research demonstrated that user’s mobility at different speeds significantly affects the network operation as a whole. The instability of users' positions leads to a significant increase in route search time and packet transmission time. Among researched GPSR, DSDV, and AODV protocols, the latter proved to be the best because it has the lowest percentage of data loss and the lowest average time of message send and receive operations. Conclusions. The work is dedicated to the actual problem of developing and setting parameters of ad-hoc network. Received research results indicate the need to choose the optimal routing protocol depending on specific application conditions, such as user movement speed and network stability. The proposed solutions can be the first stage of complex processing of packets in the mobile network and justify the choice of AODV protocol as a basis for further improvement.
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Murali, T. M., Matthew D. Dyer, David Badger, Brett M. Tyler, and Michael G. Katze. "Network-Based Prediction and Analysis of HIV Dependency Factors." PLoS Computational Biology 7, no. 9 (2011): e1002164. http://dx.doi.org/10.1371/journal.pcbi.1002164.
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Ulbrich, Frank, and Mark Borman. "Extended dependency network diagrams: adding a strategic dimension." Journal of Global Operations and Strategic Sourcing 10, no. 1 (2017): 42–66. http://dx.doi.org/10.1108/jgoss-05-2016-0019.
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Purpose Organizations increasingly form or join collaborations to gain access to resources paramount for achieving a sustained competitive advantage. This paper aims to propose an extension to the established dependency network diagram (DND) technique to better facilitate analysis, design and, ultimately, strategic management of such collaborations. Design/methodology/approach Based on the resource dependence theory, the constructs of power and secondary dependency are operationalized and integrated into the original DND technique. New rules and an updated algorithm for how to construct extended DNDs are provided. Findings The value of the proposed extension of the DND technique is illustrated by analysis of an application hosting collaboration case study from the Australian financial service industry. Research limitations/implications This study provides preliminary evidence for strategically managing resource collaborations. Future research could further test empirically the usefulness of the proposed extension of the DND technique and how much it contributes to better understanding resource collaborations. Practical implications The proposed extension of the DND technique enables managers to perform a broader analysis of dependencies among participants in a collaboration, helping them to more accurately comprehend the relationships between the entities in their collaborative environment and, thus, being in a better position of strategically managing resource dependencies. Originality/value The proposed extension of the DND technique makes a central contribution to the extant literature by adding a strategic dimension to a visualization technique used to represent collaborative environments.
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CHANG, JEONG-HO, KYU-BAEK HWANG, S. JUNE OH, and BYOUNG-TAK ZHANG. "BAYESIAN NETWORK LEARNING WITH FEATURE ABSTRACTION FOR GENE-DRUG DEPENDENCY ANALYSIS." Journal of Bioinformatics and Computational Biology 03, no. 01 (2005): 61–77. http://dx.doi.org/10.1142/s0219720005000874.
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Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset () consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.
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Zhang, Bai, Huai Li, Rebecca B. Riggins, et al. "Differential dependency network analysis to identify condition-specific topological changes in biological networks." Bioinformatics 25, no. 4 (2008): 526–32. http://dx.doi.org/10.1093/bioinformatics/btn660.
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Wołowiec, Tomasz, Daniel Szybowski Szybowski, and Dariusz Prokopowicz. "METHODS OF DEVELOPMENT NETWORK ANALYSIS AS A TOOL IMPROVING EFFICIENT ORGANIZATION MANAGEMENT." International Journal of New Economics and Social Sciences 9, no. 1 (2019): 231–51. http://dx.doi.org/10.5604/01.3001.0013.3046.
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The analysis of the dependence network consists in the calculation of dates and time reserves of subsequent events, and then on the calculation of the time stocks during the execution of particular activities. In the dependency network, it is possible to calculate the earliest and the latest possible date of occurrence of each event and the possible reserve of time. Thanks to this, we will learn the stock of time related to individual activities. Those activities that do not have a stock of time (that is, the reserve is equal to zero) are called critical activities. All critical activities in the dependence network create a critical path. We will not have any stock of time on the entire critical path (from the event of the initial linking network to the final event). What's the conclusion? Activities that are on the critical path must be made at the scheduled time, because it will decide on keeping the deadline for the entire undertaking.
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Wang, Junjie, and Qing Wang. "Analyzing and predicting software integration bugs using network analysis on requirements dependency network." Requirements Engineering 21, no. 2 (2014): 161–84. http://dx.doi.org/10.1007/s00766-014-0215-x.
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Zhou, Fang, Yanchao Du, Yongbo Yuan, and Mingyuan Zhang. "The cross-networks impact analysis and assessment in multilayer interdependent networks: A case study of critical infrastructures." International Journal of Modern Physics C 30, no. 07 (2019): 1940007. http://dx.doi.org/10.1142/s0129183119400072.
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Critical infrastructures are tightly connected and extremely fragile multilayer coupled networks. This paper discusses the cross-networks impact of subnetworks and global network of networks on robustness by taking a critical infrastructures with three-layer interdependent networks as an example. The percolation theory is applied to capture the flow characteristics of cascading failures and evaluate the robustness of multilayer networks. And further discuss and compare the situation of each subnetwork affecting or being affected. The quantitative evaluation model of the interaction of multilayer networks is proposed based on cascading failures, where the influence expansion matrix and the dependency matrix are obtained. The results show that the power network has a high influence on other networks, and it is difficult to be affected. Meanwhile the influence ability of water network and gas network is limited.
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Wang, Rongcun, Rubing Huang, and Binbin Qu. "Network-Based Analysis of Software Change Propagation." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/237243.
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The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.
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Zhang, Wang Xun, Yue Wang, and Qun Li. "An Improved Functional Dependency Network Model for SoS Operability Analysis." Applied Mechanics and Materials 602-605 (August 2014): 3355–58. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3355.
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There are often complex dependency relationships between systems in a system of systems (SoS), which present great challenges for SoS operability analysis. Functional Dependency Network Analysis offers the capability to evaluate the effect of both topology and of the possible degraded functioning of one or more systems on the operability of each node in the SoS. But there are some insufficiencies in the method, so here we give exact definition to self-effectiveness, which includes absolute self-effectiveness and relative self-effectiveness. Then according to the new definitions we show the detail calculation equations. Finally we we take the application of Guariniello and DeLaurentis’s five-node aerospace network for example, and compare the results with theirs. Comparative results show that their results are larger than the exact outcome, and our version give the authentic outcomes.
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Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.
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The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.
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Wang, Yue, Wang Xun Zhang, and Qun Li. "Functional Dependency Network Analysis of Security of Navigation Satellite System." Applied Mechanics and Materials 522-524 (February 2014): 1192–96. http://dx.doi.org/10.4028/www.scientific.net/amm.522-524.1192.
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Satellite navigation systems are running in complex electromagnetic and space environment. There is few research studies the threat and protect ability of navigation system. Lacking of qualitative data makes it difficult to analyse the security of it. In this paper, we applied Functional Dependency Network Analysis (FDNA) to solve this problem. FDNA studies how the impact caused directly by attack spreads in the overall system through the dependencies between function nodes of system. Then we are able to assess the operability of the application of navigation system. This method avoid considerable statistic experiments. Make full use of principle data. Provide constructive decision making comments.
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Deng, Yongxin, Shiyan Liu, and Dong Zhou. "Dependency cluster analysis of urban road network based on percolation." Transportation Research Part C: Emerging Technologies 154 (September 2023): 104264. http://dx.doi.org/10.1016/j.trc.2023.104264.
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Blesa, Joaquim, Fatiha Nejjari, and Ramon Sarrate. "Robust sensor placement for leak location: analysis and design." Journal of Hydroinformatics 18, no. 1 (2015): 136–48. http://dx.doi.org/10.2166/hydro.2015.021.
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In this paper, a nominal sensor placement methodology for leak location in water distribution networks is presented. To reduce the size and the complexity of the optimization problem a clustering technique is combined with the nominal sensor placement methodology. Some of the pressure sensor placement methods for leak detection and location in water distribution networks are based on the pressure sensitivity matrix analysis. This matrix depends on the network demands, which are nondeterministic, and the leak magnitudes, that are unknown. The robustness of the nominal sensor placement methodology is investigated against the fault sensitivity matrix uncertainty. Providing upon the dependency of the leak location procedure on the network operating point, the nominal sensor placement problem is then reformulated as a multi-objective optimization for which Pareto optimal solutions are generated. The robustness study as well as the resulting robust sensor placement methodology are illustrated by means of a small academic network as well as a district metered area in the Barcelona water distribution network.
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He, Haitao, Chun Shan, Xiangmin Tian, Yalei Wei, and Guoyan Huang. "Analysis on Influential Functions in the Weighted Software Network." Security and Communication Networks 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1525186.
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Identifying influential nodes is important for software in terms of understanding the design patterns and controlling the development and the maintenance process. However, there are no efficient methods to discover them so far. Based on the invoking dependency relationships between the nodes, this paper proposes a novel approach to define the node importance for mining the influential software nodes. First, according to the multiple execution information, we construct a weighted software network (WSN) to denote the software execution dependency structure. Second, considering the invoking times and outdegree about software nodes, we improve the method PageRank and put forward the targeted algorithm FunctionRank to evaluate the node importance (NI) in weighted software network. It has higher influence when the node has lager value of NI. Finally, comparing the NI of nodes, we can obtain the most influential nodes in the software network. In addition, the experimental results show that the proposed approach has good performance in identifying the influential nodes.
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Csépányi-Fürjes, László, and László Kovács. "Efficiency Analysis of Deeplearning4J Neural Network Classifiers in Development of Transition Based Dependency Parsers." Acta Marisiensis. Seria Technologica 18, no. 1 (2021): 33–39. http://dx.doi.org/10.2478/amset-2021-0006.
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Abstract Dependency parsing is a complex process in natural language text processing, text to semantic transformation. The efficiency improvement of dependency parsing is a current and an active research area in the NLP community. The paper presents four transition-based dependency parser models with implementation using DL4J classifiers. The efficiency of the proposed models were tested with Hungarian language corpora. The parsing model uses a data representation form based on lightweight embedding and a novel morphological-description-vector format is proposed for the input layer. Based on the test experiments on parsing Hungarian text documents, the proposed list-based transitions parsers outperform the widespread stack-based variants.
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Xie, Xuan, Fuyuan Zhang, Xinwen Hu, and Lei Ma. "DeepGemini: Verifying Dependency Fairness for Deep Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15251–59. http://dx.doi.org/10.1609/aaai.v37i12.26779.
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Deep neural networks (DNNs) have been widely adopted in many decision-making industrial applications. Their fairness issues, i.e., whether there exist unintended biases in the DNN, receive much attention and become critical concerns, which can directly cause negative impacts in our daily life and potentially undermine the fairness of our society, especially with their increasing deployment at an unprecedented speed. Recently, some early attempts have been made to provide fairness assurance of DNNs, such as fairness testing, which aims at finding discriminatory samples empirically, and fairness certification, which develops sound but not complete analysis to certify the fairness of DNNs. Nevertheless, how to formally compute discriminatory samples and fairness scores (i.e., the percentage of fair input space), is still largely uninvestigated. In this paper, we propose DeepGemini, a novel fairness formal analysis technique for DNNs, which contains two key components: discriminatory sample discovery and fairness score computation. To uncover discriminatory samples, we encode the fairness of DNNs as safety properties and search for discriminatory samples by means of state-of-the-art verification techniques for DNNs. This reduction enables us to be the first to formally compute discriminatory samples. To compute the fairness score, we develop counterexample guided fairness analysis, which utilizes four heuristics to efficiently approximate a lower bound of fairness score. Extensive experimental evaluations demonstrate the effectiveness and efficiency of DeepGemini on commonly-used benchmarks, and DeepGemini outperforms state-of-the-art DNN fairness certification approaches in terms of both efficiency and scalability.
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Murtala, A., N. M. Asrah, and M. A. Djauhari. "Analysis of the Nigerian stock market before and after the currency note changes using social network analysis." Mathematical Modeling and Computing 12, no. 2 (2025): 650–60. https://doi.org/10.23939/mmc2025.02.650.
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This paper examines the dependency of stocks in the Nigerian stock market from 2022 to 2023, focusing on the impact of currency note changes in government policy. The study employs the forest of all minimum-spanning trees derived from the correlation matrix of the top 40 stocks across various sectors and four traditional centrality measures to identify the most influential stocks within the network. The network analysis results reveal that the sectors dominating the market before the policy change differed from those dominating after the policy change. Additionally, the analysis indicates shifts in dominance and dependency relationships among the stocks. These findings provide valuable insights for policymakers and market participants, offering a clearer understanding of inter-sector stock relationships and facilitating the identification of stocks with positive or negative correlations.
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Wang, Jing, Youguo Li, Yusong Tan, Qingbo Wu, and Quanyuan Wu. "Analysis of Open Source Operating System Evolution: A Perspective from Package Dependency Network Motif." Symmetry 11, no. 3 (2019): 298. http://dx.doi.org/10.3390/sym11030298.
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Complexity of open source operating systems constantly increase on account of their widespread application. It is increasingly difficult to understand the collaboration between components in the system. Extant research of open source operating system evolution is mainly achieved by Lehman’s law, which is conducted by analyzing characteristics such as line of the source code. Networks, which are utilized to demonstrate relationships among entities, is an adequate model for exploring cooperation of units that form a software system. Software network has become a research hotspot in the field of software engineering, leading to a new viewpoint for us to estimate evolution of open source operating systems. Motif, a connected subgraph that occurs frequently in a network, is extensively used in other scientific research such as bioscience to detect evolutionary rules. Thus, this paper constructs software package dependency network of open source software operating systems and investigates their evolutionary discipline from the perspective of the motif. Results of our experiments, which took Ubuntu Kylin as a study example, indicate a stable evolution of motif change as well as discovering structural defect in that system.
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Zhang, Wangxun, Zhifei Li, Weiping Wang, and Qun Li. "System of Systems Safety Analysis of GNSS based on Functional Dependency Network Analysis." Applied Mathematics & Information Sciences 10, no. 6 (2016): 2227–35. http://dx.doi.org/10.18576/amis/100625.
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Lorenzo-Valdes, Arturo. "NETWORK ANALYSIS OF THE MEXICAN STOCK MARKET." Investigación Económica 83, no. 328 (2024): 55–78. http://dx.doi.org/10.22201/fe.01851667p.2024.328.87209.
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This study investigates the dynamics of equity networks in Mexico from 2018 to 2023, focusing on the impact of the COVID-19 pandemic. Methodological steps include calculating stock returns, estimating annual GARCH models, constructing lower-tailed dependency matrices, and forming networks based on these matrices. The characteristics of the resulting networks are described. In addition, 10,000 Erdos-Reyni simulations are performed to estimate GNAR models up to order two, selecting the best estimates according to AIC, BIC, and llk criteria. The predictive performance of GNAR models compared to univariate AR and VAR models is evaluated. These stages help to better understand the interconnection between Mexican financial markets, offering valuable insights for risk management and decision-making.
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Wu, Haiyan, Zhiqiang Zhang, Shaoyun Shi, Qingfeng Wu, and Haiyu Song. "Phrase dependency relational graph attention network for Aspect-based Sentiment Analysis." Knowledge-Based Systems 236 (January 2022): 107736. http://dx.doi.org/10.1016/j.knosys.2021.107736.
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Wu, Haiyan, Chaogeng Huang, and Shengchun Deng. "Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network." Information Fusion 92 (April 2023): 289–99. http://dx.doi.org/10.1016/j.inffus.2022.12.004.
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Musgrave, John, Alina Campan, Temesguen Messay-Kebede, David Kapp, and Boyang Wang. "Empirical Network Structure of Malicious Programs." Advances in Artificial Intelligence and Machine Learning 04, no. 01 (2024): 1959–76. http://dx.doi.org/10.54364/aaiml.2024.41112.
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A modern binary executable is a composition of various types of networks. Control flow graphs are a commonly used representation of an executable program used for classification tasks. Control flow and term frequency representations are widely adopted, but provide only a partial view of program semantics and present challenges to increases in resolution. By performing a quantitative analysis of program networks, we enable the identification of patterns within these features that are correlated to structure. This allows for increases in feature resolution and pattern recognition in classification tasks. These are necessary steps in order to obtain greater explainability in classification results. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. An increase in feature resolution allows for the structural
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Nanda, Ipseeta, Monika Singh, and Lizina Khatua. "Data Security and Anonymization in Neighborhood Attacks in Clustered Network in Internet of Things (NIoT)." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (2022): 28–32. http://dx.doi.org/10.17762/ijritcc.v10i11.5776.
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In this paper author tries to focus on the review on the K Nearest Neighbor (KNN) tied by one or more specific types of inter dependency, such as values, visions, ideas, financial exchange, friendship, conflict, or trade. Social network analysis views social relationships in terms of nodes and ties. It also focuses the network analysis, application as well as problem statement. In this paper presents a outline for the privacy hazard and sharing the anonymized data in the network. This includes a proposed architecture design flow, for which the author considers the several variations and make connections. On several real-world social networks, we show that simple anonymization techniques are inadequate, it results in considerable breaks of privacy for even modestly informed opponents. It also concentrates on a new anonymization technique. It based on the network and validate analytically that leads to saving of the privacy threat. It also analyses the effect that anonymizing the network has on the utility of the data for social network analysis.
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Lim, Huat Chye, Rigney Turnham, Katherine Lo, Yeonjoo Hwang, and John Gordan. "Abstract PR01: Computational identification of targetable dependencies in hepatocellular carcinoma (HCC) associated with hepatitis B virus (HBV) replication." Clinical Cancer Research 28, no. 17_Supplement (2022): PR01. http://dx.doi.org/10.1158/1557-3265.liverca22-pr01.
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Abstract Background: HBV-associated HCC has distinct molecular characteristics and worse outcomes compared to non-HBV HCC. We hypothesized that HBV infection might introduce targetable dependencies specific to HBV-associated HCC. Here, we demonstrate the utility of a computational strategy integrating genomic, network and survival analysis for identifying potential therapeutic targets in HBV-associated HCC. Methods: RNA-Seq and CRISPR dependency screening data for 22 HCC cell lines were obtained from the Cancer Dependency Map (DepMap). Cell lines were classified as HBV RNA+ if they harbored more than 40 HBV RNA reads, measured using GATK PathSeq software. Separately, whole genome CRISPRi dependency screening was performed in Hep3B and SNU-368 cells with doxycycline (dox)- inducible HBx expression. Genes where HBV RNA+ status (for DepMap) or HBx expression (for dox-inducible Hep3B/SNU-368) were significantly associated with negative dependency score were classified as HBV differential dependencies. To identify shared targets across datasets, these dependencies were evaluated using two network analysis techniques: the MCODE graph clustering algorithm and network propagation. Finally, RNA-Seq and survival data for 371 HCC cases were obtained from TCGA, and for each gene, a Cox multivariate model was used to evaluate whether the combination of decreased RNA-Seq expression and HBV RNA+ status was associated with increased survival. Results: 10 of 22 DepMap HCC cell lines were HBV RNA+. From DepMap and dox-inducible Hep3B/SNU-368 dependency screening data, 674 HBV differential dependencies were identified (Wilcoxon p < 0.05 for all). MCODE analysis identified enriched gene networks harboring multiple dependencies, including clusters associated with chromatin remodeling, the DNA damage response, and MAPK/ERK and Wnt signaling. Network propagation identified 236 enriched genes (empiric p < 0.005 for all), including the histone deacetylase HDAC1 and transcription factors ELK1, HNF4A and NRF1. Notably, HDAC1 was significant in both the MCODE and network propagation analyses, and in multivariate analysis of TCGA clinical data, decreased HDAC1 expression was associated with increased survival in HBV RNA+ HCC cases (HR for death 0.10, 95% CI 0.03-0.40, p = 0.0009). Conclusions: Measurement of HBV RNA in HCC cell lines identified a set of HBV- associated differential gene dependencies for which network analysis showed enrichment of several genes/gene clusters, including HDAC1, ELK1, HNF4A and NRF1. Survival analysis highlighted HDAC1 as a potential target where decreased expression was associated with increased survival in HBV RNA+ HCC cases in TCGA. Beyond nominating HDAC1, this study demonstrates the utility of a computational strategy integrating genomic, network and survival analysis for evaluating therapeutic targets in a virus-associated cancer. Citation Format: Huat Chye Lim, Rigney Turnham, Katherine Lo, Yeonjoo Hwang, John Gordan. Computational identification of targetable dependencies in hepatocellular carcinoma (HCC) associated with hepatitis B virus (HBV) replication [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr PR01.
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Awwaluddin, Faiznanda, Ahmad Shobrun Jamil, Siti Rofida, and M. Artabah Muchlisin. "The Therapeutic Role of Olea europaea in Alcohol Dependence Base in Network Pharmacology Analysis." Proceedings of International Pharmacy Ulul Albab Conference and Seminar (PLANAR) 3 (November 13, 2023): 201. http://dx.doi.org/10.18860/planar.v3i0.2486.
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Alcohol dependence is a state of alcohol becoming a vital part of the life of a person who consumes it; when discontinued, it can lead to a wide range of physical and mental health disorders as well as a decrease in life productivity in people with alcohol dependence. Olea Europaea (OE) is a plant capable of treating alcohol dependence. The method used in silico-based pharmacological grid analysis to determine the ability of the OE compound to treat alcohol dependence. EO compound data is obtained from the KnapSack database, absorption, distribution, metabolism, and excretion (ADME) screening using SwissADME, target protein prediction using SwissTargetPrediction, Gene cards, venny, pharmacological grid analysis with String-DB, visualization with Cytoscape 3.10.0. Results are obtained from 63 OE compounds, and 17 have ADME criteria matching the drug compounder (Drug Likeness/DL). The pathways that correlate with therapy are dopamine receptors, dopamine transporter, serotonin receptor, gamma-aminobutyric acid receptor, and toll-like receptors for known therapeutic target proteins: OPRM1, DRD2, ALDH2, ADH1B, ADH1A, ADH1C, ADH4, ADH7, SLC6A3, CNR1, POMC, ARRB2, and NCS1. Compounds associated with alcohol dependency therapy include Hexanal, Nonadienal, Octanal, 3-Hexenal, 3-Methyl-butanal, Methyl nominate, Cinchonidine, cinchonine, (9S)-10,11-Dihydrocinchonan-9-ol, Oleuropeic acid, Butyl acetate, cis-3-hexenyl acetate, and (S)-2,3-Epoxysqualene. Based on the findings, OE is a potential drug candidate for alcohol dependence.
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Huang, Weihao, Shaohua Cai, Haoran Li, and Qianhua Cai. "Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis." International Journal of Data Warehousing and Mining 19, no. 1 (2023): 1–20. http://dx.doi.org/10.4018/ijdwm.321107.
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The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.
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Minea, Marius, Cătălin Marian Dumitrescu, and Viviana Laetitia Minea. "Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions." Sensors 21, no. 15 (2021): 5036. http://dx.doi.org/10.3390/s21155036.
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The article presents a research in the field of complex sensing, detection, and recovery of communications networks applications and hardware, in case of failures, maloperations, or unauthorized intrusions. A case study, based on Davis AI engine operation versus human maintenance operation is performed on the efficiency of artificial intelligence agents in detecting faulty operation, in the context of growing complexity of communications networks, and the perspective of future development of internet of things, big data, smart cities, and connected vehicles. (*). In the second part of the article, a new solution is proposed for the detection of applications faults or unauthorized intrusions in traffic of communications networks. The first objective of the proposed method is to propose an approach for predicting time series. This approach is based on a multi-resolution decomposition of the signals employing the undecimate wavelet transform (UWT). The second approach for assessing traffic flow is based on the analysis of long-range dependence (LRD) (for this case, a long-term dependence). Estimating the degree of long-range dependence is performed by estimating the Hurst parameter of the analyzed time series. This is a relatively new statistical concept in communications traffic analysis and can be implemented using UWT. This property has important implications for network performance, design, and sizing. The presence of long-range dependency in network traffic is assumed to have a significant impact on network performance, and the occurrence of LRD can be the result of faults that occur during certain periods. The strategy chosen for this purpose is based on long-term dependence on traffic, and for the prediction of faults occurrence, a predictive control model (MPC) is proposed, combined with a neural network with radial function (RBF). It is demonstrated via simulations that, in the case of communications traffic, time location is the most important feature of the proposed algorithm.
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SELIVORSTOVA, TATJANA, SERGEY KLISHCH, SERHII KYRYCHENKO, ANTON GUDA, and KATERYNA OSTROVSKAYA. "ANALYSIS OF MONOLITHIC AND MICROSERVICE ARCHITECTURES FEATURES AND METRICS." Computer systems and information technologies, no. 3 (April 14, 2022): 59–65. http://dx.doi.org/10.31891/csit-2021-5-8.
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In this paper the information technologies stack is presented. Thesetechnologies are used during network architecture deployment. The analysis of technological advantages and drawbacks under investigation for monolithic and network architectures will be useful during of cyber security analysis in telecom networks. The analysis of the main numeric characteristics was carried out with the aid of Kubectl. The results of a series of numerical experiments on the evaluation of the response speed to requests and the fault tolerance are presented. The characteristics of the of monolithic and microservice-based architectures scalability are under investigation. For the time series sets, which characterize the network server load, the value of the Hurst exponent was calculated.
 The research main goal is the monolithic and microservice architecture main characteristics analysis, time series data from the network server accruing, and their statistical analysis.
 The methodology of Kubernetes clusters deploying using Minikube, Kubectl, Docker has been used. Application deploy on AWS ECS virtual machine with monolithic architecture and on the Kubernetes cluster (AWS EKS) were conducted.
 The investigation results gives us the confirmation, that the microservices architecture would be more fault tolerance and flexible in comparison with the monolithic architecture. Time series fractal analysis on the server equipment load showed the presence of long-term dependency, so that we can treat the traffic implementation as a self-similar process.
 The scientific novelty of the article lies in the application of fractal analysis to real time series: use of the kernel in user space, kernel latency, RAM usage, caching of RAM collected over 6 months with a step of 10 seconds, establishing a long-term dependence of time series data.
 The practical significance of the research is methodology creation of the monolithic and microservice architectures deployment and exploitation, as well as the use of time series fractal analysis for the network equipment load exploration.
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Narantuya, Jargalsaikhan, Taejin Ha, Jaewon Bae, and Hyuk Lim. "Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center." Applied Sciences 9, no. 16 (2019): 3223. http://dx.doi.org/10.3390/app9163223.
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In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.
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Chen, Xuhua. "Semantic Matching Efficiency of Supply and Demand Text on Cross-Border E-Commerce Online Technology Trading Platforms." Wireless Communications and Mobile Computing 2021 (May 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/9976774.
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With the innovation of global trade business models, more and more foreign trade companies are transforming and developing in the direction of cross-border e-commerce. However, due to the limitation of platform language processing and analysis technology, foreign trade companies encounter many bottlenecks in the process of transformation and upgrading. From the perspective of the semantic matching efficiency of e-commerce platforms, this paper improves the logical and technical problems of cross-border e-commerce in the operation process and uses semantic matching efficiency as the research object to conduct experiments on the QQP dataset. We propose a graph network text semantic analysis model TextSGN based on semantic dependency analysis for the problem that the existing text semantic matching method does not consider the semantic dependency information between words in the text and requires a large amount of training data. The model first analyzes the semantic dependence of the text and performs word embedding and one-hot encoding on the nodes (single words) and edges (dependencies) in the semantic dependence graph. On this basis, in order to quickly mine semantic dependencies, an SGN network block is proposed. The network block defines the way of information transmission from the structural level to update the nodes and edges in the graph, thereby quickly mining semantics dependent information allows the network to converge faster, train classification models on multiple public datasets, and perform classification tests. The experimental results show that the accuracy rate of TextSGN model in short text classification reaches 95.2%, which is 3.6% higher than the suboptimal classification method; the accuracy rate is 86.16%, the F 1 value is 88.77%, and the result is better than other methods.
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Zheng, Meina, Feng Liu, Xiucheng Guo, and Juchen Li. "Empirical Analysis for Impact of High-Speed Rail Construction on Interregional Dependency." Applied Sciences 10, no. 15 (2020): 5247. http://dx.doi.org/10.3390/app10155247.
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The opening of the high-speed rail (HSR) resulted in significant changes in the transportation network of Korea. The new HSR construction was expected to become a new engine of local economic growth. However, there was a controversy regarding whether the connection between regions intensifies the concentration of socio-economic activities in the metropolis (straw effect) or contributes to the balance of regional development (sprawl effect). More increasing attention had been devoted to studying the “straw effects” caused by the newly built HSR networks on interregional social-economic activities. Despite considerable research on the benefit achieved from HSR construction, little has focused on the negative externalities resulting from it. This paper examined the potential “straw effects” of two new HSR lines through constructing the indicator of interregional dependency that measured one city’s level of dependency on another one. In order to exclude the interference of lurking variables, five metropolitan cities were selected as case studies. The empirical results, based on a panel data model, revealed that the larger the economic scale of the target city, the lower the level of dependency on other cities, and there existed a “straw effect” on HSR development in terms of Seoul.
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Li, Yanying, Youlong Yang, Wensheng Wang, and Wenming Yang. "An Algorithm for Learning the Skeleton of Large Bayesian Network." International Journal on Artificial Intelligence Tools 24, no. 04 (2015): 1550012. http://dx.doi.org/10.1142/s0218213015500128.
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It is well known that Bayesian network structure learning from data is an NP-hard problem. Learning a correct skeleton of a DAG is the foundation of dependency analysis algorithms for this problem. Considering the unreliability of the high order condition independence (CI) tests and the aim to improve the efficiency of a dependency analysis algorithm, the key steps are to use less number of CI tests and reduce the sizes of condition sets as many as possible. Based on these analyses and inspired by the algorithm HPC, we present an algorithm, named efficient hybrid parents and child (EHPC), for learning the adjacent neighbors of every variable. We proof the validity of the algorithm. Compared with state-of-the-art algorithms, the experimental results show that EHPC can handle large network and has better accuracy with fewer number of condition independence tests and smaller size of conditioning set.
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Du, Mengyang, and Hongbin Wang. "DRGCN Multitasking for Aspect-Based Sentiment Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 29, no. 2 (2025): 268–76. https://doi.org/10.20965/jaciii.2025.p0268.
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Existing aspect-based sentiment analysis (ABSA) methods do not sufficiently enhance multiple subtasks with syntactic knowledge in a joint framework. In this paper, we propose an ABSA method that utilizes a multitask learning framework to enhance syntactic knowledge fully. The method first builds on a dependency relation embedded graph convolutional network to learn syntactic dependencies and the dependency types between words in a sentence fully. Second, to make better use of the syntactic information between aspect and opinion words, we extend the adjacency matrix based on dependency parsing to establish the direct relationship between aspect and opinion words. Finally, an information passing mechanism is exploited to ensure that our model learns from multiple related tasks in a multitask learning framework. The results of experiments on three public datasets, namely LAP14, REST14, and REST15, show that the proposed method has better performance than the baseline method.
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Baiyou, Qiao, Kok Kwang Phoon, Yang Lu, and Jiang Youwen. "A text sentiment analysis method based on BiGRU and capsule network." Insights of Automation in Manufacturing 1, no. 1 (2024): 165–77. http://dx.doi.org/10.59782/iam.v1i1.239.
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Most of the existing sentiment analysis methods based on product review texts rarely consider the aspect features of the review texts, and the relevant analysis models do not consider the long-term contextual dependency features and local text features at the same time, thus affecting the accuracy of sentiment analysis. A text sentiment analysis method based on a bidirectional gated recurrent network (BiGRU) and a capsule network is proposed. This method first uses a word frequency statistics-based method to extract the aspect features of the review text and integrates them into the word vector representation, thereby effectively improving the expressive power of the word vector. Then, BiGRU is used to extract the long-term contextual dependency features of the text, and the capsule network is used to extract the local features of the text, thereby achieving high-precision text sentiment analysis based on aspects. Experimental results on real datasets show that the proposed method is superior to existing sentiment analysis models such as bidirectional long short-term memory network (BiLSTM), CNN-LSTM, and TextCNN in terms of evaluation indicators such as accuracy, precision, recall, and score.
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Nadhiroh, Irene Muflikh, Ria Hardiyati, Mia Amelia, and Tri Handayani. "Mathematics and statistics related studies in Indonesia using co-authorship network analysis." International Journal of Advances in Intelligent Informatics 4, no. 2 (2018): 142. http://dx.doi.org/10.26555/ijain.v4i2.120.
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Indonesian scholars have published a numbers of articles in numerous international publications, however, it still lags behind other Singapore, Malaysia, and Vietnam. This article performs a bibliometrics analysis and examine the collaboration network in Mathematics and Statistics related subject of scholars with Indonesian affiliation as recorded in Web of Science. In total, based on article publications during 2009-2017, 426 articles were retrieved. Bandung Institute of Technology (ITB) was the affiliation with the highest number of articles (48%) and number of authors (27%). Using Social Network Analysis to examine co-authorship networks, this research shows that the co-author network has the highest centrality in the ITB affiliation. Meanwhile, dependency of foreign affiliation is still high, shown as a high percentage (84% of all articles) of international co-authorship. Co-authorship network of Mathematics and Statistics related studies in Indonesia possesses as a scale-free network and followed the power law distribution. This research showed the achievement of Indonesian scholars of Mathematics and Statistics, and can be used to evaluate the knowledge transfer in these subjects and related areas.
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Zhao, Anping, Suresh Manandhar, and Lei Yu. "Topology and semantic based topic dependency structure discovery." Filomat 32, no. 5 (2018): 1843–51. http://dx.doi.org/10.2298/fil1805843z.
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As an important enabler in achieving the maximum potential of text data analysis, topic relationship dependency structure discovery is employed to effectively support the advanced text data analysis intelligent application. The proposed framework combines an analysis approach of complex network and the Latent Dirichlet Allocation (LDA) model for topic relationship network discovery. The approach is to identify topics of the text data based on the LDA and to discover the graphical semantic structure of the intrinsic association dependency between topics. This not only exploits the association dependency between topics but also leverages a series of upper-level semantic topics covered by the text data. The results of evaluation and experimental analysis show that the proposed method is effective and feasible. The results of the proposed work imply that the topics and relationships between them can be detected by this approach. It also provides complete semantic interpretation.
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Shan, Youcheng. "Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment." Mobile Information Systems 2023 (January 31, 2023): 1–8. http://dx.doi.org/10.1155/2023/8920094.
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Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.