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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
13024
Venue
VLDB
Year
2023
Pagerank
4.6154072e-05
Overall Rank
7,924 | 44.88%
DOI
10.14778/3587136.3587140

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