A model-based approach to attributed graph clustering
Author
Xu, Zhiqiang
Ke, Yiping
Wang, Yi
Cheng, Hong
Cheng, James
Date of Issue
2012Conference Name
International Conference on Management of Data (2012)
School
School of Computer Engineering
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.
Subject
DRNTU::Engineering::Computer science and engineering
Type
Conference Paper
Collections
http://dx.doi.org/10.1145/2213836.2213894
Get published version (via Digital Object Identifier)