Abstract
Graph clustering has been extensively studied in the past decades, which can serve many real world applications, such as community detection, big network management and protein network analysis. However, the previous studies focus mainly on clustering with graph topology information. Recently, as the advance of social networks and Web 2.0, many graph datasets produced contain both the topology and node attribute information, which are known as attributed graphs. How to effectively utilize the two types of information for clustering thus becomes a hot research topic. In this paper, we propose a new attributed graph clustering method, JWNMF, which integrates topology structure and node attributes by a new collective nonnegative matrix factorization method. On the one hand, JWNMF employs a factorization for topology structure. On the other hand, it designs a weighted factorization for nodes’ attributes, where the weights are automatically determined to discriminate informative and uninformative attributes for clustering. Experimental results on seven real-world datasets show that our method significantly outperforms state-of-the-art attributed graph clustering methods.
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Acknowledgments
The research was supported in part by NSFC under Grant Nos. 61572158 and 61602132, and Shenzhen Science and Technology Program under Grant Nos. JCYJ20160330163900579 and JSGG20150512145714247, Research Award Foundation for Outstanding Young Scientists in Shandong Province, (Grant No. 2014BSA10016), the Scientific Research Foundation of Harbin Institute of Technology at Weihai (Grant No. HIT(WH)201412).
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Huang, Z., Ye, Y., Li, X., Liu, F., Chen, H. (2017). Joint Weighted Nonnegative Matrix Factorization for Mining Attributed Graphs. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_29
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DOI: https://doi.org/10.1007/978-3-319-57454-7_29
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