Show simple item record

dc.contributor.authorMaulana, A.en
dc.contributor.authorGametto, V.en
dc.contributor.authorGarlaschelli, D.en
dc.contributor.authorYevseyeva, Irynaen
dc.contributor.authorEmmerich, M. T. M.en
dc.date.accessioned2017-03-28T10:31:12Z
dc.date.available2017-03-28T10:31:12Z
dc.date.issued2017-02-13
dc.identifier.citationMaulana A., Gametto V., Garlaschelli, D., Yevseyeva I., Emmerich M.T.M. (2017) Modularities maximization in multiplex network analysis using many-objective optimization. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). December 6-9, 2016 Athens, Greece, pp 1-8.en
dc.identifier.isbn9781509042401
dc.identifier.urihttp://hdl.handle.net/2086/13907
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractAbstract: Nowadays, social network analysis receives big attention from academia, industries and governments. Some practical applications such as community detection and centrality in economic networks have become main issues in this research area. Community detection algorithm for complex network analysis is mainly accomplished by the Louvain Method that seeks to find communities by heuristically finding a partitioning with maximal modularity. Traditionally, community detection applied for a network that has homogeneous semantics, for instance indicating friend relationship between people or import-export relationships between countries etc. However we increasingly deal with more complex network and also with so-called multiplex networks. In a multiplex network the set of nodes stays the same, while there are multiple sets of edges. In the analysis we would like to identify communities, but different edge sets give rise to different modularity optimizing partitions into communities. We propose to view community detection of such multilayer networks as a many-objective optimization problem. For this apply Evolutionary Many Objective Optimization and compute the Pareto fronts between different modularity layers. Then we group the objective functions into community in order to better understand the relationship and dependence between different layers (conflict, indifference, complementarily). As a case study, we compute the Pareto fronts for model problems and for economic data sets in order to show how to find the network modularity tradeoffs between different layers.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleModularities maximization in multiplex network analysis using many-objective optimizationen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/SSCI.2016.7850231
dc.researchgroupCyber Security Centreen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.researchinstituteCyber Technology Institute (CTI)en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record