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)
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Authors
- 1. Yun Wang
- 2. Chrysanthi Kosyfaki
- 3. Sihem Amer-Yahia
- 4. Reynold Cheng
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