NDLI: Linear-time enumeration of maximal K-edge-connected subgraphs in large networks by random contraction

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Linear-time enumeration of maximal K-edge-connected subgraphs in large networks by random contraction

Content Provider	ACM Digital Library
Author	Yoshida, Yuichi Akiba, Takuya Iwata, Yoichi
Abstract	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-edge-connected subgraphs was recently proposed to address this issue. Although we can obtain better clusters with this idea, the state-of-the-art method is not efficient enough to process large networks with millions of vertices. In this paper, we propose a new method to decompose a graph into maximal k-edge-connected components, based on random contraction of edges. Our method is simple to implement but improves performance drastically. We experimentally show that our method can successfully decompose large networks and it is thousands times faster than the previous method. Also, we theoretically explain why our method is efficient in practice. To see the importance of maximal k-edge-connected subgraphs, we also conduct experiments using real-world networks to show that many k-core components have small edge-connectivity and they can be decomposed into a lot of maximal k-edge-connected subgraphs.
Starting Page	909
Ending Page	918
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505751
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Maximal k-edge-connected subgraph Cohesive subgraphs Connectivity Graphs Random contraction Vertex clusters
Content Type	Text
Resource Type	Article

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