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|Title:||A model-based approach to attributed graph clustering||Authors:||Xu, Zhiqiang
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||Source:||Xu, Z., Ke, Y., Wang, Y., Cheng, H., & Cheng, J. (2012). A model-based approach to attributed graph clustering. Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 505-516.||Abstract:||Graph clustering, also known as community detection, is a long-standing problem in data mining. However, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage structural and attribute information for clustering attributed graphs becomes a new challenge. Most existing works take a distance-based approach. They proposed various distance measures to combine structural and attribute information. In this paper, we consider an alternative view and propose a model-based approach to attributed graph clustering. We develop a Bayesian probabilistic model for attributed graphs. The model provides a principled and natural framework for capturing both structural and attribute aspects of a graph, while avoiding the artificial design of a distance measure. Clustering with the proposed model can be transformed into a probabilistic inference problem, for which we devise an efficient variational algorithm. Experimental results on large real-world datasets demonstrate that our method significantly outperforms the state-of-art distance-based attributed graph clustering method.||URI:||https://hdl.handle.net/10356/98766
|DOI:||10.1145/2213836.2213894||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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