Hypothesis testing is a statistical method used to draw conclusions about
populations from sample data, typically represented in tables. With the
prevalence of graph representations in real-life applications, hypothesis
testing in graphs is gaining importance. In this work, we formalize node, edge,
and path hypotheses in attributed graphs. We develop a sampling-based
hypothesis testing framework, which can accommodate existing
hypothesis-agnostic graph sampling methods. To achieve accurate and efficient
sampling, we then propose a Path-Hypothesis-Aware SamplEr, PHASE, an m-
dimensional random walk that accounts for the paths specified in a hypothesis.
We further optimize its time efficiency and propose PHASEopt. Experiments on
real datasets demonstrate the ability of our framework to leverage common graph
sampling methods for hypothesis testing, and the superiority of
hypothesis-aware sampling in terms of accuracy and time efficiency.

这篇论文介绍了基于假设的图采样和假设测试框架，通过路径假设感知采样方法 PHASE 提高准确性和时间效率。实验证明，该框架能够有效利用常见的图采样方法进行假设测试，并且在准确性和时间效率方面具有优势。