SubISO: A Scalable and Novel Approach for Subgraph Isomorphism Search in Large Graph

Abstract

Querying large graphs to retrieve information in permissible time is an emerging research problem, and it has roots in various application domains, mainly to analyse large networks. For a given query graph, the aim of subgraph isomorphism finding in a data graph is to identify all its subgraphs that are isomorphic to the query graph, and it has become the central problem for querying large graphs. Though different research groups have proposed many techniques using graph compression, postponing cartesian products, and candidate region exploration in the recent past, most of them show exponential behaviour for some query graphs on a large data graph. In this paper, we propose a subgraph isomorphism finding method, SubISO, which uses an objective function based on the eccentricity and some isomorphic invariants of the vertices of the query graph to minimize the number and size of the candidate regions in the data graph. Since subgraph isomorphism finding has a large number of solutions, especially in a large graph with repeated node labels, we propose to limit the maximum number of recursive calls of the generic subgraph search function to complete the execution of SubISO and return at most k-relevant matches. The SubISO finds at most k-relevant solutions in reasonable elapsed time over the queries for which existing state-of-the-art methods show exponential behaviour.