Heterogeneous Information Networks Embedding Based on Multiple Meta-Graph Fusion

Network embedding methods based on meta-structures (such as meta-path or meta-graph) can effectively utilize heterogeneous network structures. Compared with the meta-path, the meta-graph can capture more complex structural information and help improve the accuracy of similar node matching in heterogeneous information networks. However, the existing meta-graph-based embedding method typically has the following limitations: 1)Most of the meta-graph types are specified by experts, and are not applicable in the application environment of large complex networks; 2)Although multiple meta-graphs are integrated for embedding, the weights of meta-graphs are not considered; 3)Some models use the users expected semantic relationship to generate a combination of meta-graphs that can preserve specific semantics, but such models are over-reliant on meta-pattern selection and samples used to supervise learning, lacking versatility. Based on this, this paper proposes a heterogeneous network embedding method based on multiple meta-graph fusion. The method includes two parts. The first part is graph discovery. The purpose of graph discovery is to mine important meta-graphs representing the current network structure and semantic features. The second part is node embedding based on multiple meta graph fusion. The main content is to propose a general graph similarity measure method based on meta-graphs, and use the neural network to embed the meta-graph features of nodes. Experimental results show that the proposed method has higher accuracy and efficiency compared with other network embedding methods.