Paper Info
Title
Intrinsically dynamic network communities
Abstract
Community finding algorithms for networks have recently been extended to dynamic data. Most of these recent methods aim at exhibiting community partitions from successive graph snapshots and thereafter connecting or smoothing these partitions using clever time-dependent features and sampling techniques. These approaches are nonetheless achieving longitudinal rather than dynamic community detection. We assume that communities are fundamentally defined by the repetition of interactions among a set of nodes over time. According to this definition, analyzing the data by considering successive snapshots induces a significant loss of information: we suggest that it blurs essentially dynamic phenomena-such as communities based on repeated inter-temporal interactions, nodes switching from a community to another across time, or the possibility that a community survives while its members are being integrally replaced over a longer time period. We propose a formalism which aims at tackling this issue in the context of time-directed datasets (such as citation networks), and present several illustrations of both empirical and synthetic dynamic networks. We eventually introduce intrinsically dynamic metrics to qualify temporal community structure and emphasize their possible role as an estimator of the quality of the community detection-taking into account the fact that various empirical contexts may call for distinct 'community' definitions and detection criteria.
Year
DOI
Venue
2012
10.1016/j.comnet.2011.10.024
Computer Networks: The International Journal of Computer and Telecommunications Networking
Keywords
DocType
Volume
Community detection,Dynamic networks,Citation networks,Community quality metrics
Journal
56
Issue
ISSN
Citations 
3
1389-1286
8
PageRank 
References 
Authors
0.59
18
3
Name
Order
Citations
PageRank
Bivas Mitra19825.41
Lionel Tabourier2908.85
Camille Roth31579.61