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Linear-time enumeration of maximal K-edge-connected subgraphs in large networks by random contraction
2013
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13
Capturing sets of closely related vertices from large networks is an essential task in many applications such as social network analysis, bioinformatics, and web link research. Decomposing a graph into k-core components is a standard and efficient method for this task, but obtained clusters might not be well-connected. The idea of using maximal k-edgeconnected subgraphs was recently proposed to address this issue. Although we can obtain better clusters with this idea, the state-of-the-art
doi:10.1145/2505515.2505751
dblp:conf/cikm/AkibaIY13
fatcat:it647wnp4ne7dbleooreizekgq