Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Introduction to Variational Methods for Graphical Models
Machine Learning
Central Clustering of Attributed Graphs
Machine Learning
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
ACM attributed graph clustering for learning classes of images
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Clustering based on random graph model embedding vertex features
Pattern Recognition Letters
Clustering Large Attributed Graphs: A Balance between Structural and Attribute Similarities
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering Large Attributed Graphs: An Efficient Incremental Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Social influence based clustering of heterogeneous information networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and attributes community detection benchmark and a novel selection method
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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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.