Detecting highly overlapping community structure by greedy clique expansion
|Title:||Detecting highly overlapping community structure by greedy clique expansion||Authors:||Lee, Conrad
Hurley, Neil J.
|Permanent link:||http://hdl.handle.net/10197/2516||Date:||25-Jul-2010||Abstract:||In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies distinct cliques as seeds and expands these seeds by greedily optimizing a local fitness function. We perform extensive benchmarks on synthetic data to demonstrate that GCE's good performance is robust across diverse graph topologies. Significantly, GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities. Furthermore, when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Keywords:||Community Assignment;Social networks;Overlapping;Local custering algorithm;Complex networks||Subject LCSH:||Database management
Online social networks
|Language:||en||Status of Item:||Peer reviewed||Conference Details:||Paper presented at the 4th SNA-KDD Workshop ’10 (SNA-KDD’10), held in conjunction with The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), July 25, 2010, Washington, DC USA|
|Appears in Collections:||Computer Science Research Collection|
Clique Research Collection
CASL Research Collection
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