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Scalable and Effective Bipartite Network Embedding

Summary: GEBE offers a generic BNE for bipartite graphs, preserving multi-hop proximity between U and V with two measures: Poisson/Geometric/Uniform. A unified objective with efficiency tricks enables scalable training; GEBEp (Poisson-based) delivers top-N and link-prediction gains with speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6353
Venue
SIGMOD
Year
2022
Pagerank
6.2211042e-05
Overall Rank
5,432 | 62.74%
DOI
10.1145/3514221.3517838

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yang_sigmod22,
        title = {{Scalable and Effective Bipartite Network Embedding}},
        author = {Yang, Renchi and Shi, Jieming and Huang, Keke and Xiao, Xiaokui},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517838},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517838},
        year = {2022}
}

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