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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
h692b8da17aef2827
Venue
SIGMOD
Year
2012
Pagerank
8.1241746e-05
Overall Rank
2,692 | 81.91%
DOI
10.1145/2213836.2213894

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Authors

BibTeX Citation

@inproceedings{xu_sigmod12,
        title = {{A Model-based Approach to Attributed Graph Clustering}},
        author = {Xu, Zhiqiang and Ke, Yiping and Wang, Yi and Cheng, Hong and Cheng, James},
        series = {{SIGMOD} '12},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2213836.2213894},
        url = {https://dl.acm.org/doi/10.1145/2213836.2213894},
        year = {2012}
}

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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
375 Graph Clustering Based on Structural/Attribute Similarities 2009 VLDB 0.00019628745
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