Large-scale spectral clustering on graphs

Jialu Liu, Chi Wang, Marina Danilevsky, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graph clustering has received growing attention in recent years as an important analytical technique, both due to the prevalence of graph data, and the usefulness of graph structures for exploiting intrinsic data characteristics. However, as graph data grows in scale, it becomes increasingly more challenging to identify clusters. In this paper we propose an efficient clustering algorithm for largescale graph data using spectral methods. The key idea is to repeatedly generate a small number of "supernodes" connected to the regular nodes, in order to compress the original graph into a sparse bipartite graph. By clustering the bipartite graph using spectral methods, we are able to greatly improve efficiency without losing considerable clustering power. Extensive experiments show the effectiveness and efficiency of our approach.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages1486-1492
Number of pages7
StatePublished - Dec 1 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

ASJC Scopus subject areas

  • Artificial Intelligence

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