A Sampling-based Framework for Hypothesis Testing on Large Attributed
  Graphs
Yun Wang Chrysanthi Kosyfaki Sihem Amer-Yahia Reynold Cheng
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.