VerSaChI: Finding Statistically Significant Subgraph Matches using Chebyshev's Inequality

TL;DR

VerSaChI introduces a statistically grounded method for approximate subgraph matching that improves accuracy and noise robustness by leveraging Chebyshev's inequality to assess match significance.

Contribution

It presents VerSaChI, a novel approach using Chebyshev's inequality for statistically significant subgraph matching based on label and structural similarity.

Findings

Significant accuracy improvements over existing methods

Enhanced robustness to noise in graph data

Effective identification of top-k similar subgraphs

Abstract

Approximate subgraph matching, which is an important primitive for many applications like question answering, community detection, and motif discovery, often involves large labeled graphs such as knowledge graphs, social networks, and protein sequences. Effective methods for extracting matching subgraphs, in terms of label and structural similarities to a query, should depict accuracy, computational efficiency, and robustness to noise. In this paper, we propose VerSaChI for finding the top-k most similar subgraphs based on 2-hop label and structural overlap similarity with the query. The similarity is characterized using Chebyshev's inequality to compute the chi-square statistical significance for measuring the degree of matching of the subgraphs. Experiments on real-life graph datasets showcase significant improvements in terms of accuracy compared to state-of-the-art methods, as well as robustness to noise.