A Graph Matching Approach by Aggregated Search

Over the last decade, graphs have gained in popularity and have been widely used to model complex real world structures and data in various application domains. This leads to a rapid proliferation of large graph databases with hundreds of millions of nodes and edges. Thus, graph querying becomes a major problem that emerges from these collections of graphs. However, the current approaches for graph matching fail if no single graph satisfies all requirements of a query. In this paper, we present a novel approach for graph matching, where a new graph is constructed from fragments of other graphs. As a result, several relevant graph fragments are assembled and presented as solution. The proposed approach returns the best aggregated graph that fulfils as much as possible the query requirements, instead of returning a ranked list of graphs. We analyse the execution time of the proposed solution and conduct experimental evaluations to measure its effectiveness compared to several graph matching algorithms. The results show the good performance of the proposed approach.