Proceedings

Large-Scale Spectral Clustering on Graphs

Jialu Liu, Chi Wang, Marina Danilevsky, Jiawei Han

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1486-1491.

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 large-scale 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.

Jialu Liu, Chi Wang, Marina Danilevsky, and Jiawei Han. 2013. Large-Scale Spectral Clustering on Graphs. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 1486–1491. https://ijcai.org/proceedings/2013/222
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