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AGIS: Fast Approximate Graph Pattern Mining with Structure-Informed Sampling

Summary: AGIS uses structure-informed neighbor sampling, deriving and approximating an ideal pattern-aware sampling distribution instead of uniform sampling. Adaptive tradeoff between convergence and overhead yields huge speedups (28.5× mean, up to 100k×) and scales to graphs with tens of billions of edges for arbitrary patterns. (summarized by gpt-5-mini on Mar 13 2026)

Paper ID
14314
Venue
VLDB
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,276 | 28.52%
DOI
10.14778/3773749.3773761

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