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A Model-based Approach to Attributed Graph Clustering

Summary: Model-based Bayesian framework for attributed graph clustering, unifying structure and attributes without ad hoc distance design. Efficient variational inference scales to large graphs and yields superior clustering, beating state-of-the-art distance-based methods on real networks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4549
Venue
SIGMOD
Year
2012
Pagerank
8.0905959e-05
Overall Rank
2,807 | 80.48%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
313 Graph Clustering Based on Structural/Attribute Similarities 2009 VLDB 0.00028097557
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