Efficient Subgraph Isomorphism Finding in Large Graphs using Eccentricity and Limiting Recursive Calls

Abstract: The subgraph isomorphism finding problem is a well-studied problem in the field of computer science and graph theory, and it aims to enumerate all instances of a query graph in the respective data graph. In this paper, we propose an efficient method, SubISO, to find subgraph isomorphisms using an objective function, which exploits some isomorphic invariants and eccentricity of the query graph's vertices. The proposed objective function is used to determine pivot vertex, which minimizes both number and size of the candidate regions in the data graph. SubISO also limits the maximum recursive calls of the generic SubgraphSearch function to deal with straggler queries for which most of the existing algorithms show exponential behaviour. The proposed approach is evaluated over three benchmark datasets. It is also compared with three well known subgraph isomorphism finding algorithms in terms of execution time, number of identified embeddings, and ability to deal with the straggler queries, and it performs significantly better.