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Efficient Estimation of Heat Kernel PageRank for Local Clustering

Summary: TEA and TEA+: HKPR-based local clustering with relative-error guarantees and near-linear time in cluster size, via deterministic rough HKPR and Monte Carlo refinement. TEA+ beats prior methods ~4x on real graphs (Twitter, Friendster), enabling scalable clustering on billion-edge networks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5760
Venue
SIGMOD
Year
2019
Pagerank
6.0439971e-05
Overall Rank
5,913 | 59.44%
DOI
10.1145/3299869.3319886

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yang_sigmod19,
        title = {{Efficient Estimation of Heat Kernel PageRank for Local Clustering}},
        author = {Yang, Renchi and Xiao, Xiaokui and Wei, Zhewei and Bhowmick, Sourav S and Zhao, Jun and Li, Rong-Hua},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3319886},
        url = {https://dl.acm.org/doi/10.1145/3299869.3319886},
        year = {2019}
}

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