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.Comment: 10 pages, 7 Figures. Implementation source and binaries available at
  http://sites.google.com/site/greedycliqueexpansion

Hurley, Neil

Lee, Conrad

McDaid, Aaron

Reid, Fergal

English

arXiv

In complex networks it is common for each node to belong to sev-eral communities, implying a highly overlapping community struc-ture. Recent advances in benchmarking indicate that the exist-ing community assignment algorithms that are capable of detect-ing 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 opti-mizing 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 inter-action data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively

Conrad Lee

Fergal Reid

Aaron Mcdaid

Neil Hurley

CiteSeerX

Detecting highly overlapping community structure by greedy clique expansion

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 USAIn 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.Science Foundation IrelandConference detailshttp://www.snakdd.com/When published, contact AV: set text with citation and DOI need to be added to PDF - AV 11/10/2010. au ke SB. 11/10/2010
Previous proceedings avail. at http://portal.acm.org/event.cfm?id=RE329
Not included in published proceedings - 14/03/11 O

Hurley, Neil J.

Research Repository UCD

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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 USAIn 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\u27s 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.Science Foundation IrelandConference detailshttp://www.snakdd.com/When published, contact AV: set text with citation and DOI need to be added to PDF - AV 11/10/2010. au ke SB. 11/10/2010
Previous proceedings avail. at http://portal.acm.org/event.cfm?id=RE329
Not included in published proceedings - 14/03/11 O

Irish Universities

Detecting highly overlapping community structure by greedy clique
  expansion

Detecting highly overlapping community structure by greedy clique expansion

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