Options
Detecting highly overlapping community structure by greedy clique expansion
Date Issued
2010-07-25
Date Available
2010-10-13T15:16:19Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subject – LCSH
Database management
Computer algorithms
Online social networks
Language
English
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
This item is made available under a Creative Commons License
File(s)
Owning collection
Views
2563
Last Month
2
2
Acquisition Date
Mar 19, 2024
Mar 19, 2024
Downloads
1753
Last Week
1
1
Last Month
5
5
Acquisition Date
Mar 19, 2024
Mar 19, 2024