A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs
This work addresses hypothesis testing for researchers and practitioners dealing with graph data, but it is incremental as it builds on existing graph sampling methods.
The paper tackled hypothesis testing on large attributed graphs by formalizing node, edge, and path hypotheses and developing a sampling-based framework, with results showing that their proposed hypothesis-aware sampler (PHASE and PHASEopt) achieved superior accuracy and time efficiency in experiments on real datasets.
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.