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Статті в журналах з теми "Weighted Complex Network"
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Lan, Wang Sen, and Guo Hao Zhao. "Detecting Backbone of Weighted Complex Network." Advanced Materials Research 143-144 (October 2010): 712–16. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.712.
Повний текст джерелаАнотація:
In order to explore key nodes natures and find out the core of weighted networks, the study advanced backbone network (BN) conception, developed largest eigenvalue algorithm of weight matrix (LEAWM) which utilized matrix characteristic spectrum to detect BN nodes, and done empirical research for two networks: (1) US air lines network, (2) stocks network of coal and power sectors in china stock market. The empirical results indicate that LEAWM is efficient for detecting the BN nodes with some important properties such as bigger degree and betweenness, BN is the core and backbone of its mother network.
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Dai, Meifeng, Yongbo Hou, Tingting Ju, Changxi Dai, Yu Sun, and Weiyi Su. "Weighted trapping time of weighted directed treelike network." International Journal of Modern Physics C 31, no. 08 (July 10, 2020): 2050108. http://dx.doi.org/10.1142/s0129183120501089.
Повний текст джерелаАнотація:
With the deepening of research on complex networks, many properties of complex networks are gradually studied, for example, the mean first-passage times, the average receive times and the trapping times. In this paper, we further study the average trapping time of the weighted directed treelike network constructed by an iterative way. Firstly, we introduce our model inspired by trade network, each edge [Formula: see text] in undirected network is replaced by two directed edges with weights [Formula: see text] and [Formula: see text]. Then, the trap located at central node, we calculate the weighted directed trapping time (WDTT) and the average weighted directed trapping time (AWDTT). Remarkably, the WDTT has different formulas for even generations and odd generations. Finally, we analyze different cases for weight factors of weighted directed treelike network.
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Mohmand, Yasir Tariq, and Aihu Wang. "Weighted Complex Network Analysis of Pakistan Highways." Discrete Dynamics in Nature and Society 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/862612.
Повний текст джерелаАнотація:
The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.
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Xu, Shuang, Chunxia Zhang, Pei Wang, and Jiangshe Zhang. "Variational Bayesian weighted complex network reconstruction." Information Sciences 521 (June 2020): 291–306. http://dx.doi.org/10.1016/j.ins.2020.02.050.
Повний текст джерела
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XU, XIN-JIAN, ZHI-XI WU, and YING-HAI WANG. "PROPERTIES OF WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics C 17, no. 04 (April 2006): 521–29. http://dx.doi.org/10.1142/s0129183106008662.
Повний текст джерелаАнотація:
We study two kinds of weighted networks, weighted small-world (WSW) and weighted scale-free (WSF). The weight wij of a link between nodes i and j in the network is defined as the product of endpoint node degrees; that is wij =(ki kj)θ. In contrast to adding weights to links when networks are being constructed, we only consider weights depending on the "popularity" of the nodes represented by their connectivity. It was found that both weighted networks have broad distributions on the characterization of the link weight, the vertex strength, and the average shortest path length. Furthermore, as a survey of the model, the epidemic spreading process in both weighted networks was studied based on the standard susceptible-infected (SI) model. The spreading velocity reaches a peak very quickly after the infection outbreaks and an exponential decay was found in the long time propagation.
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LEUNG, C. C., and H. F. CHAU. "WEIGHTED ACCELERATED GROWTH MODEL OF COMPLEX NETWORKS." International Journal of Modern Physics B 21, no. 23n24 (September 30, 2007): 4064–66. http://dx.doi.org/10.1142/s0217979207045219.
Повний текст джерелаАнотація:
We introduce and study a toy model which mimics the structure formation of a typical weighted network in the real world. In particular, the organizational structures of our networks are found to be consistent with real-world networks.
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WANG, XUTAO, HONGTAO LU, and GUANRONG CHEN. "THE MODELLING OF WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics B 21, no. 16 (June 20, 2007): 2813–20. http://dx.doi.org/10.1142/s0217979207037399.
Повний текст джерелаАнотація:
In order to further explore the mechanism responsible for weighted complex networks, we introduce a new model that incorporates the network topology and the weights' dynamical evolutions. Our model can capture the details of weight dynamics caused not only by the addition of a new node with new links and new links between old nodes, but also the deletion of old links. We calculate analytically the distributions of both degree and strength and found that all these distributions show scale-free behavior, as confirmed in many real networks. Thus our model characterizes the real weighted complex networks more precisely.
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Long, Hao, and Xiao-Wei Liu. "Multiresolution community detection in weighted complex networks." International Journal of Modern Physics C 30, no. 02n03 (February 2019): 1950016. http://dx.doi.org/10.1142/s0129183119500165.
Повний текст джерелаАнотація:
The community is the dominant structure that exhibits different features and multifold functions of complex networks from different levels; accordingly, multiresolution community detection is of critical importance in network science. In this paper, inspired by the ideas of the network flow, we propose an intensity-based community detection algorithm, i.e. ICDA, to detect multiresolution communities in weighted networks. First, the edge intensity is defined to quantify the relationship between each pair of connected nodes, and the vertices connected by the edges with higher intensities are denoted as core nodes, while the others are denoted as marginal nodes. Second, by applying the expansion strategy, the algorithm merges the closely connected core nodes as the initial communities and attaches marginal nodes to the nearest initial communities. To guarantee a higher internal density for the ultimate communities, the captured communities are further adjusted according to their densities. Experimental results of real and synthetic networks illustrate that our approach has higher performance and better accuracy. Meanwhile, a multiresolution investigation of some real networks shows that the algorithm can provide hierarchical details of complex networks with different thresholds.
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Xing, Yingying, Jian Lu, and Shendi Chen. "Weighted Complex Network Analysis of Shanghai Rail Transit System." Discrete Dynamics in Nature and Society 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1290138.
Повний текст джерелаАнотація:
With increasing passenger flows and construction scale, Shanghai rail transit system (RTS) has entered a new era of networking operation. In addition, the structure and properties of the RTS network have great implications for urban traffic planning, design, and management. Thus, it is necessary to acquire their network properties and impacts. In this paper, the Shanghai RTS, as well as passenger flows, will be investigated by using complex network theory. Both the topological and dynamic properties of the RTS network are analyzed and the largest connected cluster is introduced to assess the reliability and robustness of the RTS network. Simulation results show that the distribution of nodes strength exhibits a power-law behavior and Shanghai RTS network shows a strong weighted rich-club effect. This study also indicates that the intentional attacks are more detrimental to the RTS network than to the random weighted network, but the random attacks can cause slightly more damage to the random weighted network than to the RTS network. Our results provide a richer view of complex weighted networks in real world and possibilities of risk analysis and policy decisions for the RTS operation department.
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Nguyen, Quang, Ngoc-Kim-Khanh Nguyen, Davide Cassi, and Michele Bellingeri. "New Betweenness Centrality Node Attack Strategies for Real-World Complex Weighted Networks." Complexity 2021 (October 15, 2021): 1–17. http://dx.doi.org/10.1155/2021/1677445.
Повний текст джерелаАнотація:
In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.
Дисертації з теми "Weighted Complex Network"
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McAndrew, Thomas Charles. "Weighted Networks: Applications from Power grid construction to crowd control." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/668.
Повний текст джерелаАнотація:
Since their discovery in the 1950's by Erdos and Renyi, network theory (the study of objects and their associations) has blossomed into a full-fledged branch of mathematics.
Due to the network's flexibility, diverse scientific problems can be reformulated as networks and studied using a common set of tools.
I define a network G = (V,E) composed of two parts: (i) the set of objects V, called nodes, and (ii) set of relationships (associations) E, called links, that connect objects in V.
We can extend the classic network of nodes and links by describing the intensity of these associations with weights.
More formally, weighted networks augment the classic network with a function f(e) from links to the real line, uncovering powerful ways to model real-world applications.
This thesis studies new ways to construct robust micro powergrids, mine people's perceptions of causality on a social network, and proposes a new way to analyze crowdsourcing all in the context of the weighted network model.
The current state of Earth's ecosystem and intensifying climate calls on scientists to find new ways to harvest clean affordable energy.
A microgrid, or neighborhood-scale powergrid built using renewable energy sources attached to personal homes, suggest one way to ameliorate this energy crisis.
We can study the stability (robustness) of such a small-scale system with weighted networks.
A novel use of weighted networks and percolation theory guides the safe and efficient construction of power lines (links, E) connecting a small set of houses (nodes, V) to one another and weights each power line by the distance between houses.
This new look at the robustness of microgrid structures calls into question the efficacy of the traditional utility.
The next study uses the twitter social network to compare and contrast causal language from everyday conversation.
Collecting a set of 1 million tweets, we find a set of words (unigrams), parts of speech, named entities, and sentiment signal the use of informal causal language.
Breaking a problem difficult for a computer to solve into many parts and distributing these tasks to a group of humans to solve is called Crowdsourcing.
My final project asks volunteers to 'reply' to questions asked of them and 'supply' novel questions for others to answer.
I model this 'reply and supply' framework as a dynamic weighted network, proposing new theories about this network's behavior and how to steer it toward worthy goals.
This thesis demonstrates novel uses of, enhances the current scientific literature on, and presents novel methodology for, weighted networks.
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Sekgoka, Chaka Patrick. "Modeling cross-border financial flows using a network theoretic approach." Thesis, University of Pretoria, 2021. http://hdl.handle.net/2263/78773.
Повний текст джерелаАнотація:
Criminal networks exploit vulnerabilities in the global financial system, using it as a conduit to launder criminal proceeds. Law enforcement agencies, financial institutions, and regulatory organizations often scrutinize voluminous financial records for suspicious activities and criminal conduct as part of anti-money laundering investigations. However, such studies are narrowly focused on incidents and triggered by tip-offs rather than data mining insights.
This research models cross-border financial flows using a network theoretic approach and proposes a symmetric-key encryption algorithm to preserve information privacy in multi-dimensional data sets. The newly developed tools will enable regulatory organizations, financial institutions, and law enforcement agencies to identify suspicious activity and criminal conduct in cross-border financial transactions.
Anti-money laundering, which comprises laws, regulations, and procedures to combat money laundering, requires financial institutions to verify and identify their customers in various circumstances and monitor suspicious activity transactions. Instituting anti-money laundering laws and regulations in a country carries the benefit of creating a data-rich environment, thereby facilitating non-classical analytical strategies and tools.
Graph theory offers an elegant way of representing cross-border payments/receipts between resident and non-resident parties (nodes), with links representing the parties' transactions. The network representations provide potent data mining tools, facilitating a better understanding of transactional patterns that may constitute suspicious transactions and criminal conduct.
Using network science to analyze large and complex data sets to detect anomalies in the data set is fast becoming more important and exciting than merely learning about its structure. This research leverages advanced technology to construct and visualize the cross-border financial flows' network structure, using a directed and dual-weighted bipartite graph.
Furthermore, the develops a centrality measure for the proposed cross-border financial flows network using a method based on matrix multiplication to answer the question, "Which resident/non-resident nodes are the most important in the cross-border financial flows network?" The answer to this question provides data mining insights about the network structure.
The proposed network structure, centrality measure, and characterization using degree distributions can enable financial institutions and regulatory organizations to identify dominant nodes in complex multi-dimensional data sets. Most importantly, the results showed that the research provides transaction monitoring capabilities that allow the setting of customer segmentation criteria, complementing the built-in transaction-specific triggers methods for detecting suspicious activity transactions.Thesis (PhD)--University of Pretoria, 2021.
Banking Sector Education and Training Authority (BANKSETA)
UP Postgraduate Bursary
Industrial and Systems Engineering
PhD
Unrestricted
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Rui, Yikang. "Urban Growth Modeling Based on Land-use Changes and Road Network Expansion." Doctoral thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122182.
Повний текст джерелаАнотація:
A city is considered as a complex system. It consists of numerous interactivesub-systems and is affected by diverse factors including governmental landpolicies, population growth, transportation infrastructure, and market behavior.Land use and transportation systems are considered as the two most importantsubsystems determining urban form and structure in the long term. Meanwhile,urban growth is one of the most important topics in urban studies, and its maindriving forces are population growth and transportation development. Modelingand simulation are believed to be powerful tools to explore the mechanisms ofurban evolution and provide planning support in growth management. The overall objective of the thesis is to analyze and model urban growth basedon the simulation of land-use changes and the modeling of road networkexpansion. Since most previous urban growth models apply fixed transportnetworks, the evolution of road networks was particularly modeled. Besides,urban growth modeling is an interdisciplinary field, so this thesis made bigefforts to integrate knowledge and methods from other scientific and technicalareas to advance geographical information science, especially the aspects ofnetwork analysis and modeling. A multi-agent system was applied to model urban growth in Toronto whenpopulation growth is considered as being the main driving factor of urbangrowth. Agents were adopted to simulate different types of interactiveindividuals who promote urban expansion. The multi-agent model with spatiotemporalallocation criterions was shown effectiveness in simulation. Then, anurban growth model for long-term simulation was developed by integratingland-use development with procedural road network modeling. The dynamicidealized traffic flow estimated by the space syntax metric was not only used forselecting major roads, but also for calculating accessibility in land-usesimulation. The model was applied in the city centre of Stockholm andconfirmed the reciprocal influence between land use and street network duringthe long-term growth. To further study network growth modeling, a novel weighted network model,involving nonlinear growth and neighboring connections, was built from theperspective of promising complex networks. Both mathematical analysis andnumerical simulation were examined in the evolution process, and the effects ofneighboring connections were particular investigated to study the preferentialattachment mechanisms in the evolution. Since road network is a weightedplanar graph, the growth model for urban street networks was subsequentlymodeled. It succeeded in reproducing diverse patterns and each pattern wasexamined by a series of measures. The similarity between the properties of derived patterns and empirical studies implies that there is a universal growthmechanism in the evolution of urban morphology. To better understand the complicated relationship between land use and roadnetwork, centrality indices from different aspects were fully analyzed in a casestudy over Stockholm. The correlation coefficients between different land-usetypes and road network centralities suggest that various centrality indices,reflecting human activities in different ways, can capture land development andconsequently influence urban structure. The strength of this thesis lies in its interdisciplinary approaches to analyze andmodel urban growth. The integration of ‘bottom-up’ land-use simulation androad network growth model in urban growth simulation is the major contribution.The road network growth model in terms of complex network science is anothercontribution to advance spatial network modeling within the field of GIScience.The works in this thesis vary from a novel theoretical weighted network modelto the particular models of land use, urban street network and hybrid urbangrowth, and to the specific applications and statistical analysis in real cases.These models help to improve our understanding of urban growth phenomenaand urban morphological evolution through long-term simulations. Thesimulation results can further support urban planning and growth management.The study of hybrid models integrating methods and techniques frommultidisciplinary fields has attracted a lot attention and still needs constantefforts in near future.QC 20130514
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Wang, Danling. "Multifractal characterisation and analysis of complex networks." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48176/1/Danling_Wang_Thesis.pdf.
Повний текст джерелаАнотація:
Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.
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Bringeland, Nathalie. "DNA methylation correlation networks in overweight and normal-weight adolescents reveal differential coordination." Thesis, Uppsala universitet, Funktionell farmakologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202863.
Повний текст джерелаАнотація:
Multiple health issues are associated with obesity and numerous factors are causative of the disease. The role of genetic factors is well established, as is the knowledge that dietary and sedentary behavior promotes weight gain. Although there is strong suspicion towards the role of epigenetics as a driving force toward disease, this field remains l in the context of obesity. DNA methylation correlation networks were profiled from blood samples of 69 adolescents of two distinct weight-classes; obese (n=35) and normal-weight (n=34). The network analysis revealed major differences in the organization of the networks where the network of the obese had less modularity compared to normal-weight. This is manifested by more and smaller clusters in the obese, pertaining to genes of related functions and pathways, than the network of the normal-weight. Consequently, this suggests that biological pathways have a lower order of coordination between each other in means of DNA methylation in obese than normal-weight. Analysis of highly connected genes, hubs, in the two networks suggests that the difference in coordination between biological pathways may be derived by changes of the methylation pattern of these hubs; highly connected genes in one network had an intriguingly low connectivity in the other. In conclusion, the results suggest differential regulation of transcription through changes in the coordination of DNA methylation in overweight and normal weighted individuals. The findings of this study are a major step towards understanding the role of DNA methylation in obesity and provide potential biomarkers for diagnosing and predicting obesity.
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El, Haj Abir. "Stochastics blockmodels, classifications and applications." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2300.
Повний текст джерелаАнотація:
Cette thèse de doctorat porte sur l’analyse de réseaux pondérés, graphes finis où chaque arête est associée à un poids représentant l’intensité de sa force. Nous introduisons une extension du modèle à blocs stochastiques (SBM) binaire, appelée modèle à blocs stochastiques binomial (bSBM). Cette question est motivée par l’étude des réseaux de co-citations dans un contexte de fouille de textes où les données sont représentées par un graphe. Les noeuds sont des mots et chaque arête joignant deux mots est pondérée par le nombre de documents inclus dans le corpus citant simultanément cette paire de mots. Nous développons une méthode d’inférence basée sur l’algorithme espérance maximisation variationnel (EMV) pour estimer les paramètres du modèle proposé ainsi que pour classifier les mots du réseau. Puis nous adoptons une méthode qui repose sur la maximisation d’un critère ICL (en anglais integrated classification likelihood) pour sélectionner le modèle optimal et le nombre de clusters. D’autre part, nous développons une approche variationnelle pour traiter le réseau et nous comparons les deux approches. Des applications à des données réelles sont adoptées pour montrer l’efficacité des deux méthodes ainsi que pour les comparer. Enfin, nous développons un SBM avec plusieurs attributs pour traiter les réseaux ayant des poids associés aux noeuds. Nous motivons cette méthode par une application qui vise au développement d’un outil d’aide à la spécification de différents traitements cognitifs réalisés par le cerveau lors de la préparation à l’écritureThis PhD thesis focuses on the analysis of weighted networks, where each edge is associated to a weight representing its strength. We introduce an extension of the binary stochastic block model (SBM), called binomial stochastic block model (bSBM). This question is motivated by the study of co-citation networks in a context of text mining where data is represented by a graph. Nodes are words and each edge joining two words is weighted by the number of documents included in the corpus simultaneously citing this pair of words. We develop an inference method based on a variational maximization algorithm (VEM) to estimate the parameters of the modelas well as to classify the words of the network. Then, we adopt a method based on maximizing an integrated classification likelihood (ICL) criterion to select the optimal model and the number of clusters. Otherwise, we develop a variational approach to analyze the given network. Then we compare the two approaches. Applications based on real data are adopted to show the effectiveness of the two methods as well as to compare them. Finally, we develop a SBM model with several attributes to deal with node-weighted networks. We motivate this approach by an application that aims at the development of a tool to help the specification of different cognitive treatments performed by the brain during the preparation of the writing
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Supriya, Supriya. "Brain Signal Analysis and Classification by Developing New Complex Network Techniques." Thesis, 2020. https://vuir.vu.edu.au/40551/.
Повний текст джерелаАнотація:
Brain signal analysis has a crucial role in the investigation of the neuronal activity for diagnosis of brain diseases and disorders. The electroencephalogram (EEG) is the most efficient biomarker for the analysis of brain signal that assists in the diagnosis of brain disorder medication and also plays an essential role in all the neurosurgery related to the brain. EEG findings illustrate the meticulous condition, and clinical content of the brain dysfunctions, and has an undisputed importance role in the detection of epilepsy condition and sleep disorders and dysfunctions allied to alcohol. The clinicians visually study the EEG recording to determine the manifestation of abnormalities in the brain. The visual EEG assessment is tiresome, fallible, and also high-priced. In this dissertation, a number of frameworks have been developed for the analysis and classification of EEG signals by addressing three different domains named: Epilepsy, Sleep staging, and Alcohol Use Disorder.
Epilepsy is a non-contagious chronic disease of the brain that affects around 65 million people worldwide. The sudden onset tendency of the epileptic attacks vulnerable their sufferers to injuries. It is also challenging for the clinical staff to detect the epileptic-seizure activity early enough for determining the semiology associated with the seizure onset. For that reason, automated techniques that can accurately detect the epilepsy from EEG are of great importance to epileptic patients and especially to those patients who are resistive to therapies and medications. In this dissertation, four different techniques (named Weighted Visibility Network, Weighted Horizontal Visibility Network, Weighted Complex Network, and New Weighted Complex Network) have been developed for the automated identification of epileptic activity from the EEG signals. Most of the developed schemes attained 100% classification outcomes in their experimental evaluation for the identification of seizure activity from non-seizure activity.
A sleep disorder can increase the menace of seizure incidence or severity, cognitive tasks impairments, mood deviation, diminution in the functionality of the immune system and other brain anomalies such as insomnia, sleep apnoea, etc. Hence, sleep staging is essential to discriminate among distinct sleep stages for the diagnosis of sleep and its disorders. EEG provides vital and inimitable information regarding the sleeping brain. The study of EEG has documented deformities in sleep patterns. This research has developed an innovative graph- theory based framework named weighted visibility network for sleep staging from EEG signals. The developed framework in this thesis, outperforms with 97.93% overall classification accuracy for categorizing distinct sleep states
Alcoholism causes memory issues as well as motor skill defects by affecting the different portions of the brain. Excessive use of alcohol can cause sudden cardiac death and cardiomyopathy. Also, alcohol use disorder leads to respiratory infections, Vision impairment, liver damage, and cancer, etc. Research study demonstrates the use of EEG for diagnosis the patient with a high menace of developmental impediments with alcohol. In this current Ph.D. project, I developed a weighted graph-based technique that analyses EEG to distinguish between alcoholic subject and non-alcoholic person. The promising classification outcome demonstrates the effectiveness of the proposed technique.
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Bhattacharyya, Moitrayee. "Probing Ligand Induced Perturbations In Protien Structure Networks : Physico-Chemical Insights From MD Simulations And Graph Theory." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2341.
Повний текст джерелаАнотація:
The fidelity of biological processes and reactions, inspite of the widespread diversity, is programmed by highly specific physico-chemical principles. This underlines our basic understanding of different interesting phenomena of biological relevance, ranging from enzyme specificity to allosteric communication, from selection of fold to structural organization / states of oligomerization, from half-sites-reactivity to reshuffling of the conformational free energy landscape, encompassing the dogma of sequence-structure dynamics-function of macromolecules. The role of striking an optimal balance between rigidity and flexibility in macromolecular 3D structural organisation is yet another concept that needs attention from the functional perspective. Needless to say that the variety of protein structures and conformations naturally leads to the diversity of their function and consequently many other biological functions in general. Classical models of allostery like the ‘MWC model’ or the ‘KNF model’ and the more recently proposed ‘population shift model’ have advanced our understanding of the underlying principles of long range signal transfer in macromolecules. Extensive studies have also reported the importance of the fold selection and 3D structural organisation in the context of macromolecular function. Also ligand induced conformational changes in macromolecules, both subtle and drastic, forms the basis for controlling several biological processes in an ordered manner by re-organizing the free energy landscape.
The above mentioned biological phenomena have been observed from several different biochemical and biophysical approaches. Although these processes may often seem independent of each other and are associated with regulation of specialized functions in macromolecules, it is worthwhile to investigate if they share any commonality or interdependence at the detailed atomic level of the 3D structural organisation. So the nagging question is, do these diverse biological processes have a unifying theme, when probed at a level that takes into account even subtle re-orchestrations of the interactions and energetics at the protein/nucleic acid side-chain level. This is a complex problem to address and here we have made attempts to examine this problem using computational tools. Two methods have been extensively applied: Molecular Dynamics (MD) simulations and network theory and related parameters. Network theory has been extensively used in the past in several studies, ranging from analysis of social networks to systems level networks in biology (e.g., metabolic networks) and have also found applications in the varied fields of physics, economics, cartography and psychology. More recently, this concept has been applied to study the intricate details of the structural organisation in proteins, providing a local view of molecular interactions from a global perspective. On the other hand, MD simulations capture the dynamics of interactions and the conformational space associated with a given state (e.g., different ligand-bound states) of the macromolecule. The unison of these two methods enables the detection and investigation of the energetic and geometric re-arrangements of the 3D structural organisation of macromolecule/macromolecular complexes from a dynamical or ensemble perspective and this has been one of the thrust areas of the current study. So we not only correlate structure and functions in terms of subtle changes in interactions but also bring in conformational dynamics into the picture by studying such changes along the MD ensemble.
The focus was to identify the subtle rearrangements of interactions between non-covalently interacting partners in proteins and the interacting nucleic acids. We propose that these rearrangements in interactions between residues (amino acids in proteins, nucleic acids in RNA/DNA) form the common basis for different biological phenomena which regulates several apparently unrelated processes in biology. Broadly, the major goal of this work is to elucidate the physico-chemical principles underlying some of the important biological phenomena, such as allosteric communication, ligand induced modulation of rigidity/flexibility, half-sites-reactivity and so on, in molecular details. We have investigated several proteins, protein-RNA/DNA complexes to formulate general methodologies to address these questions from a molecular perspective. In the process we have also specifically illuminated upon the mechanistic aspects of the aminoacylation reaction by aminoacyl-tRNA synthetases like tryptophanyl and pyrrolysyl tRNA synthetase, structural details related to an enzyme catalyzed reaction that influences the process of quorum sensing in bacteria. Further, we have also examined the ‘dynamic allosterism’ that manipulates the activity of MutS, a prominent component of the DNA bp ‘mismatch repair’ machinery. Additionally, our protein structure network (PSN) based studies on a dataset of Rossmann fold containing proteins have provided insights into the structural signatures that drive the adoption of a fold from a repertoire of diverse sequences. Ligand induced percolations distant from the active sites, which may be of functional relevance have also been probed, in the context of the S1A family of serine proteases. In the course of our investigation, we have borrowed several concepts of network parameters from social network analysis and have developed new concepts.
The Introduction (Chapter-1) summarizes the relevant literature and lays down a suitable background for the subsequent chapters in the thesis. The major questions addressed and the main goal of this thesis are described to set an appropriate stage for the detailed discussions. The methodologies involved are discussed in Chapter-2. Chapter-3 deals with a protein, LuxS that is involved in the bacterial quorum sensing; the first part of the chapter describes the application of network analysis on the static structures of several LuxS proteins from different organisms and the second part of this chapter describes the application of a dynamic network approach to analyze the MD trajectories of H.pylori LuxS. Chapter-4 focuses on the investigation of human tryptophanyl-tRNA synthetase (hTrpRS), with an emphasis to identify ligand induced subtle conformational changes in terms of the alternation of rigidity/flexibility at different sites and the re-organisation of the free energy landscape. Chapter-5 presents a novel application of a quantum clustering (QC) technique, popular in the fields of pattern recognition, to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. The protein structure network (PSN) in the earlier studies were constituted on the basis of geometric interactions. In Chapters 6 and 7, we describe the networks (proteins+nucleic acids) using interaction energy as edges, thus incorporating the detailed chemistry in terms of an energy-weighted complex network. Chapter-6 describes an application of the energy weighted network formalism to probe allosteric communication in D.hafniense pyrrolysyl-tRNA synthetase. The methodology developed for in-depth study of ligand induced changes in DhPylRS has been adopted to the protein MutS, the first ‘check-point protein’ for DNA base pair (bp) mismatch repair. In Chapter-7, we describe the network analysis and the biological insights derived from this study (the work is done in collaboration with Prof. David Beveridge and Dr. Susan Pieniazek). Chapter-8 describes the application of a network approach to capture the ligand-induced subtle global changes in protein structures, using a dataset of high resolution structures from the S1A family of serine proteases. Chapter-9 deals with probing the structural rationale behind diverse sequences adopting the same fold with the NAD(P)-binding Rossmann fold as a case study. Future directions are discussed in the final chapter of the thesis (Chapter-10).
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Schoen, Alexander C. "Complex Vehicle Modeling: A Data Driven Approach." Thesis, 2019. http://hdl.handle.net/1805/21466.
Повний текст джерелаАнотація:
Indiana University-Purdue University Indianapolis (IUPUI)This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks. The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model. The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.
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Wiliński, Mateusz. "Przemiany fazowe w empirycznych, korelacyjnych sieciach złożonych." Doctoral thesis, 2019. https://depotuw.ceon.pl/handle/item/3441.
Повний текст джерелаАнотація:
Rozprawa należy do nurtu badań nad Układami Złożonymi (ang. Complex Systems). Jej pierwsza część dotyczy fenomenologicznych metod badania struktury układów opisanych z użyciem wielowymiarowych szeregów czasowych. W szczególności, w pracy zaproponowano zupełnie nowe estymatory korelacji między sygnałami, przeznaczone dla danych próbkowanych w sposób nieregularny. Zostały one oparte na analizie fourierowskiej, jak również na ścisłych wyprowadzeniach uzyskanych między innymi dla procesów schodkowych. W pracy znaleźć można również autorską metodę filtrowania sieci ważonych, która jest dalej wykorzystana w celu wygenerowania sieci na podstawie otrzymanych macierzy korelacji.
Druga część rozprawy koncentruje się na przemianach fazowych obserwowanych w sieciach złożonych. Początkowo ma ona charakter monograficzny i opisuje najbardziej znane w literaturze modele sieciowe. Następnie analizowany jest autorski model spinowy na sieci z koewolucją. Jest to, według wiedzy autora, pierwszy przypadek równowagowego modelu z koewolucją.
Trzecia część pracy dotyczy empirycznych zastosowań metodologii opisanej w rozdziale drugim. Autor analizuje w niej dane finansowe oraz medyczne. Te pierwsze dotyczą dziennych oraz wewnątrzdziennych notowań giełdowych. Z kolei te drugie to sygnały EEG uzyskane zarówno od zdrowych pacjentów jak i chorych na padaczkę. Otrzymane wyniki wskazują na istnienie zjawisk krytycznych zarówno w przypadku danych finansowych jak i aktywności bioelektrycznej mózgu.The dissertation belongs to the field of Complex Systems. Its first part concentrates on phenomenological methods of analysing the structure of systems described with a multidimensional time series. In particular, a number of novel correlation estimators, designed specifically for irregularly sampled data, are proposed. The methods are based on Fourier analysis as well as on strict derivations made for step function processes among others. Additionally, the work shows a new method for filtering weighted networks, which is later used in order to generate networks from the obtained correlation matrices. The second part of the dissertation concerns the phase transitions observed for complex networks. Initially it is monographic and it describes the most influential models from the literature. Secondly, a new spin model with coevolution is proposed. The model, to the best of author’s knowledge, is the first attempt to build an equilibrium model of a coevolving network. The third part is dedicated to using the methodology presented in chapter two, in order to analyse empirical data. The author studies both financial and medical data. The financial part focuses on daily and intraday data from stock exchanges. The medical part concerns EEG data gathered from both healthy patients and those with epileptic seizures. The obtained results show that there exist critical phenomena both in the case of financial markets and brain bioelectrical activity.
Книги з теми "Weighted Complex Network"
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Bianconi, Ginestra. Structural Correlations of Multiplex Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0007.
Повний текст джерелаАнотація:
Multiplex networks encode relevant information in their correlated structure, including interlayer degree correlation, link overlap, weight–topology correlations in weighted multiplex networks and activity of the nodes. Interlayer degree correlations among a pair of layers indicates for instance whether or not the hub nodes of one layer are also hub nodes in the other. Link overlap indicates that a finite fraction of nodes are connected in more than one layer. Weight–topology correlations of weighted complex networks reveal that the weight of the links is not random, but often correlated with the link overlap. Finally, the nodes of a multiplex network might be connected only in a subset of the network, leading to a heterogeneity node activity. This chapter identifies the main multiplex network measures for characterizing these correlations, and evaluates their significance using statistical and information theory methods and novel multiplex network measures, including multilinks and multidegrees.
Частини книг з теми "Weighted Complex Network"
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Rajeh, Stephany, Marinette Savonnet, Eric Leclercq, and Hocine Cherifi. "Modularity-Based Backbone Extraction in Weighted Complex Networks." In Network Science, 67–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97240-0_6.
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Zhou, Zhi, Xiaojun Zou, Xueqiang Lv, and Junfeng Hu. "Research on Weighted Complex Network Based Keywords Extraction." In Lecture Notes in Computer Science, 442–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45185-0_47.
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Gao, Zhong-Ke, Ning-De Jin, and Wen-Xu Wang. "Directed Weighted Complex Network for Characterizing Gas-Liquid Slug Flow." In Nonlinear Analysis of Gas-Water/Oil-Water Two-Phase Flow in Complex Networks, 73–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38373-1_8.
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Jian, Zhou, Zhai Qun, and Tao Jianping. "A Network Security Risk Fuzzy Clustering Assessment Model Based on Weighted Complex Network." In Computing and Intelligent Systems, 143–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24010-2_20.
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Kim, Kibae, and Jörn Altmann. "A Complex Network Analysis of the Weighted Graph of the Web2.0 Service Network." In Advances in Intelligent and Soft Computing, 79–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25321-8_7.
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Zhang, Hanyong, Qingfang Meng, Bo Meng, Mingmin Liu, and Yang Li. "Epileptic Seizure Detection Based on Time Domain Features and Weighted Complex Network." In Intelligent Computing Theories and Application, 483–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_57.
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Zhang, Hanyong, Qingfang Meng, Mingmin Liu, and Yang Li. "A New Epileptic Seizure Detection Method Based on Fusion Feature of Weighted Complex Network." In Advances in Neural Networks – ISNN 2018, 834–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92537-0_94.
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Zhang, Tian, Zhiyong Huang, Handong Wen, and Zhenfeng Bao. "An Efficiency Evaluation Model of Combat SoS Counterworks Based on Directed and Weighted Network." In Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, 413–23. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2666-9_41.
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Yang, Hua, Mingyao Zhang, Donghong Ji, and Guozheng Xiao. "Complex Query Expansion Based on Weighted Shortest Path Length in Key Term Concurrence Network." In Lecture Notes in Computer Science, 499–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45185-0_52.
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Zhang, Hanning, Bo Dong, Boqin Feng, and Haiyu Wu. "An Overlapping Community Detection Algorithm Based on Triangle Reduction Weighted for Large-Scale Complex Network." In Algorithms and Architectures for Parallel Processing, 627–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60245-1_43.
Повний текст джерелаТези доповідей конференцій з теми "Weighted Complex Network"
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Zeng, Ming, Wenkang Xu, Chunyu Zhao, Qi Li, and Jingjing Han. "Weighted Complex Network Based on Visibility Angle Measurement." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189168.
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Liang, Yin. "Chinese keyword extraction based on weighted complex network." In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2017. http://dx.doi.org/10.1109/iske.2017.8258737.
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Haley, Brandon M., Andy Dong, and Irem Y. Tumer. "Creating Faultable Network Models of Complex Engineered Systems." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34407.
Повний текст джерелаАнотація:
This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.
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Hui-Jia Li, Chi Zhang, and Xiang-Sun Zhang. "A study of inflammation immunization strategy in weighted complex network." In 11th International Symposium on Operations Research and its Applications in Engineering, Technology and Management 2013 (ISORA 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2281.
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Yang, Ting, Dinghua Zhang, Bing Chen, and Shan Li. "Analysis of Mixed Production Line Based on Complex Weighted Network." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.463.
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Panigrahi, Premananda, and Somnath Maity. "Vulnerability Analysis of Weighted Indian Power Grid Network Based on Complex Network Theory." In 2017 14th IEEE India Council International Conference (INDICON). IEEE, 2017. http://dx.doi.org/10.1109/indicon.2017.8487727.
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Xia, Wei, Xinxue Liu, Shaofei Meng, and Jinlong Fan. "Research on Node Importance Evaluation of the Directed Weighted Complex Network." In 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icence-16.2016.135.
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Wei, Jing, and Baolong Guo. "Reliability Evaluation Method of Complex Software Based on Weighted Network Model." In 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP). IEEE, 2018. http://dx.doi.org/10.1109/siprocess.2018.8600496.
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Tang, Xiwei, Jianxin Wang, Min Li, Yiming He, and Yi Pan. "A novel algorithm for mining protein complex from the weighted network." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732611.
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Yupeng Chen and Chaohuan Hou. "High resolution adaptive bearing estimation using a complex-weighted neural network." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226056.
Повний текст джерелаЗвіти організацій з теми "Weighted Complex Network"
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Patel, Reena. Complex network analysis for early detection of failure mechanisms in resilient bio-structures. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41042.
Повний текст джерелаАнотація:
Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented.
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Данильчук, Г. Б., О. А. Засядько та В. М. Соловйов. Застосування методів теорії складних систем при оцінці економічної безпеки підприємства. Видавець Вовчок О.Ю., 2017. http://dx.doi.org/10.31812/0564/1260.
Повний текст джерелаАнотація:
The paper estimated the financial stability of the enterprise «Motor Sich» network measures and using permutation entropy. The analysis and comparison of the weights with integrated measurement of financial security. The conclusions about the possibility of using methods of the theory of complex systems in assessing economic security.