DBScholar

Back to papers

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
4611
Venue
SIGMOD
Year
2012
Pagerank
8.3043854e-05
Overall Rank
2,644 | 81.87%
DOI
10.1145/2213836.2213894

Incoming Non-self Citations Over Time

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
364 Graph Clustering Based on Structural/Attribute Similarities 2009 VLDB 0.00020054172
Previous Page 1 / 1 Next

Semantically Similar Papers