Characterizing the Community Structure of Complex Networks

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Lancichinetti, Andrea Kivelä, Mikko Saramäki, Jari Fortunato, Santo 2017-05-11T09:09:13Z 2017-05-11T09:09:13Z 2010
dc.identifier.citation Lancichinetti , A , Kivelä , M , Saramäki , J & Fortunato , S 2010 , ' Characterizing the Community Structure of Complex Networks ' PLOS ONE , vol 5 , no. 8 , e11976 , pp. 1-8 . DOI: 10.1371/journal.pone.0011976 en
dc.identifier.issn 1932-6203
dc.identifier.other PURE UUID: d8724ca3-b957-4e7b-b8b8-daa49e16ef19
dc.identifier.other PURE ITEMURL:
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dc.description.abstract Background Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. Methodology/Principal Findings We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Conclusions/Significance Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks' evolution. en
dc.format.extent 1-8
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries PLOS ONE en
dc.relation.ispartofseries Volume 5, issue 8 en
dc.rights openAccess en
dc.title Characterizing the Community Structure of Complex Networks en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department School services, SCI
dc.contributor.department BECS
dc.contributor.department Department of Computer Science en
dc.identifier.urn URN:NBN:fi:aalto-201705114269
dc.identifier.doi 10.1371/journal.pone.0011976
dc.type.version publishedVersion

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