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Detecting community structure in networks via consensus dynamics and spatial transformation
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2017 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 483, 156-170 p.Article in journal (Refereed) Published
Abstract [en]

We present a novel clustering algorithm for community detection, based on the dynamics towards consensus and spatial transformation. The community detection problem is translated to a clustering problem in the N-dimensional Euclidean space by three stages: (1) the dynamics running on a network is emulated to a procedure of gas diffusion in a finite space; (2) the pressure distribution vectors are used to describe the influence that each node exerts on the whole network; (3) the similarity measures between two nodes are quantified in the N-dimensional Euclidean space by k-Nearest Neighbors method. After such steps, we could merge clusters according to their similarity distances and show the community structure of a network by a hierarchical clustering tree. Tests on several benchmark networks are presented and the results show the effectiveness and reliability of our algorithm.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 483, 156-170 p.
Keyword [en]
Clustering, Community detection, Consensus, Networks, Spatial transformation
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-209495DOI: 10.1016/j.physa.2017.04.098Scopus ID: 2-s2.0-85019065476OAI: oai:DiVA.org:kth-209495DiVA: diva2:1113037
Funder
Swedish Research Council
Note

QC 20170621

Available from: 2017-06-21 Created: 2017-06-21 Last updated: 2017-06-21Bibliographically approved

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Citation style
  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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