Use of Eigenvector Centrality to Detect Graph Isomorphism

TL;DR

This paper proposes using eigenvector centrality sequences as a heuristic to quickly identify non-isomorphic graphs, reducing the need for more complex isomorphism tests.

Contribution

It introduces a novel heuristic based on eigenvector centrality sequences to efficiently filter non-isomorphic graph pairs before applying detailed algorithms.

Findings

EVC sequences effectively distinguish non-isomorphic graphs.

The heuristic reduces the number of graphs requiring detailed isomorphism testing.

Confirmed isomorphism with Nauty for graphs with matching EVC sequences.

Abstract

Graph Isomorphism is one of the classical problems of graph theory for which no deterministic polynomial-time algorithm is currently known, but has been neither proven to be NP-complete. Several heuristic algorithms have been proposed to determine whether or not two graphs are isomorphic (i.e., structurally the same). In this research, we propose to use the sequence (either the non-decreasing or nonincreasing order) of eigenvector centrality (EVC) values of the vertices of two graphs as a precursor step to decide whether or not to further conduct tests for graph isomorphism. The eigenvector centrality of a vertex in a graph is a measure of the degree of the vertex as well as the degrees of its neighbors. We hypothesize that if the non-increasing (or non-decreasing) order of listings of the EVC values of the vertices of two test graphs are not the same, then the two graphs are not isomorphic. If two test graphs have an identical non-increasing order of the EVC sequence, then they are declared to be potentially isomorphic and confirmed through additional heuristics. We test our hypothesis on random graphs (generated according to the Erdos-Renyi model) and we observe the hypothesis to be indeed true: graph pairs that have the same sequence of non-increasing order of EVC values have been confirmed to be isomorphic using the well-known Nauty software.