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Modularity-Driven Clustering of Dynamic Graphs
[chapter]
2010
Lecture Notes in Computer Science
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In an experimental evaluation on both a real-world dynamic network and on dynamic clustered random graphs, we show that the dynamic maintenance of a clustering of a changing graph yields higher modularity ...
Our algorithms efficiently maintain a modularity-based clustering of a graph for which dynamic changes arrive as a stream. ...
Conclusion As the first work on modularity-driven clustering of dynamic graphs, we deal with the NPhard problem of updating a modularity-optimal clustering after a change in the graph. ...
doi:10.1007/978-3-642-13193-6_37
fatcat:ow2o43vomfhzno32obs6wkpawq
Organization of Excitable Dynamics in Hierarchical Biological Networks
2008
PLoS Computational Biology
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The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal ...
This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. ...
A Measure of Dynamic Modularity For computation of our new quantity assessing the match between topology and dynamics, the dynamic modularity Q dyn , we compare two clustering trees, one coming from topology ...
doi:10.1371/journal.pcbi.1000190
pmid:18818769
pmcid:PMC2542420
fatcat:t6bosbknbrg3tm6wif463ewcp4
Dynamic graph clustering combining modularity and smoothness
2013
ACM Journal of Experimental Algorithmics
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In an experimental evaluation on both a real-world dynamic network and on dynamic clustered random graphs, we show that the dynamic maintenance of a clustering of a changing graph yields higher modularity ...
Our algorithms efficiently maintain a modularity-based clustering of a graph for which dynamic changes arrive as a stream. ...
Conclusion As the first work on modularity-driven clustering of dynamic graphs, we deal with the NP-hard problem of updating a modularity-optimal clustering after a change in the graph. ...
doi:10.1145/2444016.2444021
fatcat:cpaxhctkuvfh3acfomvkw5b3fq
Context-aware Dynamic Data-driven Pattern Classification
2014
Procedia Computer Science
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This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. ...
Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. ...
Clustering using these graphs is same as community detection, where a community in a graph is a cluster of nodes with more intra-cluster edges than inter-cluster edges. ...
doi:10.1016/j.procs.2014.05.119
fatcat:oh3sh2ocyrbzzpieiemrvbzmry
Community Detection via Local Dynamic Interaction
[article]
2014
arXiv
pre-print
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(d) Attractor is easy to parameterize, since there is no need to specify the number of clusters. ...
Instead of investigating the node dynamics, we actually examine the change of "distances" among linked nodes. ...
For detailed reviews of graph clustering, please refer to [14] . Cut-Criterion Clustering. ...
arXiv:1409.7978v1
fatcat:3ko7i5dyhfec5o5avwtigvurue
PeerSim: A scalable P2P simulator
2009
2009 IEEE Ninth International Conference on Peer-to-Peer Computing
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report graph-theoretical properties of overlay topologies, such as diameter, clustering, and so on. ...
Modularity and Configuration PEERSIM was designed with modularity and ease of configuration in mind. ...
doi:10.1109/p2p.2009.5284506
dblp:conf/p2p/MontresorJ09
fatcat:rcaaunex7naq5f5lnuhlo4d66e
Towards a new approach to reveal dynamical organization of the brain using topological data analysis
2018
Nature Communications
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Most previous work has focused on analyzing changes in co-fluctuations between a set of brain regions over several temporal segments of the data. ...
Little is known about how our brains dynamically adapt for efficient functioning. ...
The second dataset was originally collected as part of the Human Connectome Project (HCP 35 ) while participants performed working-memory tasks. ...
doi:10.1038/s41467-018-03664-4
pmid:29643350
pmcid:PMC5895632
fatcat:cxbund33zjbf3ilvlenezp5rye
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
[article]
2020
arXiv
pre-print
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The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects. ...
Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions ...
of T = 240 temporal graphs {W r,t } were characterized by time-evolving modular connectivity Θ [st] driven by underlying piece-wise stationary state time course s t ∈ 1, . . . , S. ...
arXiv:2004.04362v3
fatcat:34ooiejxxvgwla76v62efiq46a
Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature
[article]
2021
arXiv
pre-print
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Here, we define a dynamic edge curvature for the study of arbitrary networks measuring the deformation between pairs of evolving dynamical network processes on different timescales. ...
Importantly, curvature gaps robustly encode communities until the phase transition of detectability, where spectral clustering methods fail. ...
Geometric modularity for the multiscale clustering of networks To exploit the property of the dynamical OR curvature to give multiple geometric representations, we develop a multiscale graph clustering ...
arXiv:2106.05847v1
fatcat:erayhxxasbb6vdkxg7irdddrxq
Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs
2018
Proceedings of the IEEE
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The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. ...
in structure and function, the organization of dynamic behavior over time, and disease related brain changes. ...
of the ...
doi:10.1109/jproc.2018.2825200
pmid:30364630
pmcid:PMC6197492
fatcat:uh6tvaymifgh3cubnkemqgcd5a
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
2020
IEEE Transactions on Medical Imaging
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The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects. ...
Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions ...
of T = 240 temporal graphs {W r,t } were characterized by time-evolving modular connectivity Θ [st] driven by underlying piece-wise stationary state time course s t ∈ 1, . . . , S. ...
doi:10.1109/tmi.2020.3030047
pmid:33044929
fatcat:fawa67nmj5ee7atmhycnarezie
LICOD: A Leader-driven algorithm for community detection in complex networks
2014
Vietnam Journal of Computer Science
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External criteria for evaluating obtained clusters can then be used for comparing performances of different community detection approaches. ...
We propose also a new way for evaluating performances of community detection algorithms. This consists on transforming data clustering problems into a community detection problems. ...
Data clustering-driven evaluation We propose here to use the task of data clustering to apply a task-driven evaluation of community detection algorithms. ...
doi:10.1007/s40595-014-0025-6
fatcat:wpj4kfp62bh7fcjcnrqtuzrb5m
The many facets of community detection in complex networks
2017
Applied Network Science
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Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. ...
Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community ...
Consequently, depending on the dynamics of interest, the modular building blocks may look different. ...
doi:10.1007/s41109-017-0023-6
pmid:30533512
pmcid:PMC6245232
fatcat:25z3qfifofhr3chmimfokxbika
Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows
2014
Social Network Analysis and Mining
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In this paper, we introduce a methodology to examine the dynamics of customer communities, which relies on two dierent time window models: a landmark and a sliding window. ...
Such approach is appropriate for the long term analysis of networks, but may fail to provide a realistic picture of the current evolution. ...
We also thank the anonymous reviewers for their valuable comments and suggestions on earlier versions of this manuscript. ...
doi:10.1007/s13278-014-0208-2
fatcat:dopl56noz5chjphmgeonuyhp3m
Static and Dynamic Aspects of Scientific Collaboration Networks
2012
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Additionally, we detect communities with modularity-based clustering and compare the resulting clusters to a ground-truth based on conferences and thus topical similarity. ...
Using the computer science publication database DBLP, we compile relations between authors and publications as graphs and proceed with examining and quantifying collaborative relations with graph-based ...
ACKNOWLEDGMENT We thank Ulrik Brandes for helpful discussions during the preparation of this work. ...
doi:10.1109/asonam.2012.90
dblp:conf/asunam/StaudtSMGW12
fatcat:niiamd47hrgt7p4czwfhiyzqda
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