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Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach

Summary: Introduces AnchorGNN, a global-local GNN for billion-scale bipartite embedding: anchor-based message passing captures global knowledge, while likelihood-based one-hop modeling avoids adjacency construction. It improves accuracy up to 36% and speed up to 28×. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13601
Venue
VLDB
Year
2024
Pagerank
5.5181056e-05
Overall Rank
7,908 | 45.75%
DOI
10.14778/3626292.3626300

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wu_vldb24,
        title = {{Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach}},
        author = {Wu, Xueyi and Xu, Yuanyuan and Zhang, Wenjie and Zhang, Ying},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {2},
        pages = {175--183},
        doi = {10.14778/3626292.3626300},
        url = {https://doi.org/10.14778/3626292.3626300},
        year = {2024}
}

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