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Algorithms 2016, 9(4), 73; doi:10.3390/a9040073

Community Structure Detection for Directed Networks through Modularity Optimisation

1
Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London WC1E 7JE, UK
2
Department of Informatics, Faculty of Natural and Mathematical Sciences, King’s College London, London WC2R 2LS, UK
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: Tatsuya Akutsu and Takeyuki Tamura
Received: 14 August 2016 / Revised: 5 October 2016 / Accepted: 18 October 2016 / Published: 1 November 2016
(This article belongs to the Special Issue Biological Networks)
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Abstract

Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is much higher than across different subgraphs. Each of these subgraphs forms a community (or module). In literature, a metric called modularity is defined that measures the quality of a partition of nodes into different mutually exclusive communities. One means of deriving community structure is modularity maximisation. In this paper, a novel mathematical programming-based model, DiMod, is proposed that tackles the problem of maximising modularity for directed networks. View Full-Text
Keywords: community detection; directed networks; modularity optimisation; integer programming; complex networks community detection; directed networks; modularity optimisation; integer programming; complex networks
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Yang, L.; Silva, J.C.; Papageorgiou, L.G.; Tsoka, S. Community Structure Detection for Directed Networks through Modularity Optimisation. Algorithms 2016, 9, 73.

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