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Distributed Graph Embedding with Information-Oriented Random Walks

Summary: DistGER scales to billion-edge graphs by using information-centric random walks and a multi-proximity streaming partitioner to maximize locality and balance. An access-locality-optimized distributed Skip-Gram yields 2.3–129× speedups, ~45% less cross-machine traffic and >10% downstream gains vs prior frameworks. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13212
Venue
VLDB
Year
2023
Pagerank
5.5193e-05
Overall Rank
7,900 | 45.80%
DOI
10.14778/3587136.3587140

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{fang_vldb23,
        title = {{Distributed Graph Embedding with Information-Oriented Random Walks}},
        author = {Fang, Peng and Khan, Arijit and Luo, Siqiang and Wang, Fang and Feng, Dan and Li, Zhenli and Yin, Wei and Cao, Yuchao},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {7},
        pages = {1643--1656},
        doi = {10.14778/3587136.3587140},
        url = {https://doi.org/10.14778/3587136.3587140},
        year = {2023}
}

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