Consensus clustering in complex networks

Andrea Lancichinetti, Santo Fortunato

Research output: Contribution to journalArticleScientificpeer-review

354 Citations (Scopus)
195 Downloads (Pure)

Abstract

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.
Original languageEnglish
Article number336
Pages (from-to)1-7
JournalScientific Reports
Volume2
DOIs
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed

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