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A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs

Summary: Formalizes node, edge, and path hypotheses on attributed graphs and presents a sampling-based hypothesis-testing framework that leverages existing graph samplers. Introduces PHASE, an m-dimensional path-hypothesis-aware random walk (and optimized PHASEopt) to improve sampling accuracy and runtime, with experiments showing superiority over hypothesis-agnostic methods. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13534
Venue
VLDB
Year
2024
Pagerank
-
Overall Rank
13,155 | 8.49%
DOI
10.14778/3681954.3681993

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