Subgraph matching symbol algorithm based on graph neural network

Subgraph matching is a fundamental problem in graph data analysis and has important research significance. Aiming at the problem of a large number of redundant searches in the subgraph matching algorithm, a subgraph matching symbol algorithm based on graph neural network(SSMGNN) was proposed. The algorithm used the graph neural network technology to aggregate the neighborhood information of nodes, and obtained the feature vector containing the local attributes and structure of the graph, and used the vector as the filter condition to obtain the node candidate set C of the query graph. In addition, optimizing the matching order and using symbolic ADD operations to construct each candidate region of C in the data graph reduced redundant searches during subgraph enumeration verification. The experimental results show that, compared with the VF3 algorithm, the algorithm effectively improve the solving efficiency of subgraph matching.