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Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs

Summary: Memory-aware framework for second-order random walks on billion-edge graphs, addressing memory blowups of conventional sampling with a cost model for node sampling. Acceptance-rejection sampling with per-node cost optimization allocates under a memory budget while minimizing time, delivering ~90% memory reduction and practical APIs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5775
Venue
SIGMOD
Year
2020
Pagerank
5.0392468e-05
Overall Rank
6,498 | 54.80%
DOI
10.1145/3318464.3380562

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