Subgraph Isomorphism Building on A Hierarchical Query Graph

Subgraph isomorphism is an essential problem of graph theory. It has broad application on information retrieval in many research field, such as biology, chemistry, knowledge graph and social network. The settlement to graph isomorphism is to find the embedding details from a given query graph Gq to a given data graph G. Till now, most subgraph isomorphism research build on searching from one fixed query graph. However, in practice, fixed query graph will lead to heavy query graph building work. In this paper, we proposed a subgraph matching framework built on a hierarchical query graph to alleviate the burden of query graph building work. The vertices of query graph are classified into three type: compulsory vertex, optional vertex and forbidden vertex. We proposed a modified VF2 algorithm to tackle this task. It creatively handle the order problem by converting the vertices matching into label matching and using a sieving method to map the label to the vertex. We conducted experiments on a financial semantic relationship datasets. Results show our modified VF2 algorithm can accurately map the query graph Gq to the subgraph of data graph G with acceptable time efficiency.