Overlapping communities in dynamic networks: their detection and mobile applications
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Simulate to Detect: A Multi-agent System for Community Detection
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A Community Based Algorithm for Deriving Users' Profiles from Egocentrics Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
OverCite: finding overlapping communities in citation network
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
CUT: community update and tracking in dynamic social networks
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
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Community detection on networks is a well-known problem encountered in many fields, for which the existing algorithms are inefficient 1) at capturing overlaps in-between communities, 2) at detecting communities having disparities in size and density 3) at taking into account the networks’ dynamics. In this paper, we propose a new algorithm (iLCD) for community detection using a radically new approach. Taking into account the dynamics of the network, it is designed for the detection of strongly overlapping communities. We first explain the main principles underlying the iLCD algorithm, introducing the two notions of intrinsic communities and longitudinal detection, and detail the algorithm. Then, we illustrate its efficiency in the case of a citation network, and then compare it with existing most efficient algorithms using a standard generator of community-based networks, the LFR benchmark.