ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (9): 1928-1938.doi: 10.7544/issn1000-1239.2020.20190553

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Heterogeneous Information Networks Embedding Based on Multiple Meta-Graph Fusion

Wu Yao, Shen Derong, Kou Yue, Nie Tiezheng, Yu Ge   

  1. (School of Computer Science and Engineering, Northeastern University, Shenyang 110169)
  • Online:2020-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61672142, U1435216), the National Key Research and Development Program of China (2018YFB1003404), the National Natural Science Foundation of China Joint Fund Project (U1811261), and the Fundamental Research Funds for the Central Universities (N171606005).

Abstract: 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.

Key words: heterogeneous information networks, network embedding, meta-graph, meta-path, fusion

CLC Number: