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 (University of Hong Kong)
- 2. Chrysanthi Kosyfaki (University of Hong Kong)
- 3. Sihem Amer-Yahia (National Centre for Scientific Research; University Grenoble Alpes)
- 4. Reynold Cheng (University of Hong Kong)
BibTeX Citation
@article{wang_vldb24,
title = {{A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs}},
author = {Wang, Yun and Kosyfaki, Chrysanthi and Amer-Yahia, Sihem and Cheng, Reynold},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3192--3200},
doi = {10.14778/3681954.3681993},
url = {https://doi.org/10.14778/3681954.3681993},
year = {2024}
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