A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs

arXiv:2403.13286v1 Announce Type: cross
Abstract: 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, …

abstract applications arxiv cs.db cs.lg data edge framework graph graphs hypothesis importance life node path sample sampling statistical statistical method stat.ml tables testing type work