  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.
