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Coarsening Massive Influence Networks for Scalable Diffusion Analysis

Summary: Coarsens large influence graphs into vertex-weighted summaries preserving diffusion properties. Two implementations—linear-time speed-focused and scalable near-linear with sublinear space—enable frameworks that accelerate influence maximization and estimation on billion-edge networks, shrinking graphs to ~4% and delivering ~4x/3.5x speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5421
Venue
SIGMOD
Year
2017
Pagerank
4.6214923e-05
Overall Rank
7,904 | 45.02%
DOI
10.1145/3035918.3064045

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