Paper Info
Title
Detection of Overlapping Communities in Dynamical Social Networks
Abstract
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.
Year
DOI
Venue
2010
10.1109/SocialCom.2010.51
SocialCom/PASSAT
Keywords
Field
DocType
existing algorithm,dynamical social networks,citation network,overlapping communities,ilcd algorithm,intrinsic community,longitudinal detection,community-based network,new algorithm,community detection,new approach,efficient algorithm,algorithm design and analysis,social network analysis,benchmark testing,robustness
Data mining,Social network,Algorithm design,Computer science,Social network analysis,ILCD,Citation network,Robustness (computer science),Benchmark (computing)
Conference
Citations 
PageRank 
References 
49
2.12
2
Authors
3
Name
Order
Citations
PageRank
Cazabet Remy11319.71
Frédéric Amblard243051.43
Chihab Hanachi318323.06