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.

翻译：子图同构查找问题是计算机科学与图论领域中的一个经典问题，其目标在于枚举数据图中查询图的所有实例。本文提出了一种高效方法SubISO，该方法利用目标函数来查找子图同构，该函数利用了查询图顶点的一些同构不变量和偏心距。所提出的目标函数用于确定枢轴顶点，从而最小化数据图中候选区域的数量和规模。SubISO还限制了通用SubgraphSearch函数的最大递归调用次数，以处理那些现有算法大多呈现指数级行为的异常查询。所提方法在三个基准数据集上进行了评估，并与三种著名的子图同构查找算法在执行时间、识别到的嵌入数量以及处理异常查询能力方面进行了比较，结果表明其性能显著更优。