An Efficient Subgraph Isomorphism Solver for Large Graphs

For a given pair of pattern and data graphs, the subgraph isomorphism finding problem locates all instances graph into graph. isomorphic image in graph, set ordered pairs graph’s vertices their respective images is called an embedding. Many solvers, such as $\mathrm {Turbo_{ISO}}$ , Glasgow VF3 exist literature for problem. Though each solver aims to minimize computing costs its own way, computational efficiency still central issue In this paper, we present development efficient solver, SubGlw which first decomposes small-size candidate subgraphs using ranking function then searches embeddings them separately. The designed way that it minimizes both number size subgraphs. performance empirically evaluated compared with two state-of-the-art solvers – SubISO over three benchmark datasets Yeast Human Hprd . experimental findings reveal performs significantly better terms embedding count execution time We have also presented analysis identifying saddle point timeout at our achieves maximum least time. This provides understanding parameter settings. source codes can be downloaded from https://github.com/ZubairAliIgraph/SubGlw-master