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Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs

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  • Canwei Liu

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China)

  • Xingye Deng

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Tingqin He

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Lei Chen

    (School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Guangyang Deng

    (School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Yuanyu Hu

    (School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

Abstract

Edge embedding is a technique for constructing low-dimensional feature vectors of edges in heterogeneous graphs, which are also called heterogeneous information networks (HINs). However, edge embedding research is still in its early stages, and few well-developed models exist. Moreover, existing models often learn features on the edge graph, which is much larger than the original network, resulting in slower speed and inaccurate performance. To address these issues, a multi-view learning-based fast edge embedding model is developed for HINs in this paper, called MVFEE. Based on the “divide and conquer” strategy, our model divides the global feature learning into multiple separate local intra-view features learning and inter-view features learning processes. More specifically, each vertex type in the edge graph (each edge type in HIN) is first treated as a view, and a private skip-gram model is used to rapidly learn the intra-view features. Then, a cross-view learning strategy is designed to further learn the inter-view features between two views. Finally, a multi-head attention mechanism is used to aggregate these local features to generate accurate global features of each edge. Extensive experiments on four datasets and three network analysis tasks show the advantages of our model.

Suggested Citation

  • Canwei Liu & Xingye Deng & Tingqin He & Lei Chen & Guangyang Deng & Yuanyu Hu, 2023. "Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs," Mathematics, MDPI, vol. 11(13), pages 1-23, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2974-:d:1186156
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    References listed on IDEAS

    as
    1. Kim, Paul & Kim, Sangwook, 2015. "Detecting overlapping and hierarchical communities in complex network using interaction-based edge clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 46-56.
    2. Cai, Biao & Wang, Yanpeng & Zeng, Lina & Hu, Yanmei & Li, Hongjun, 2020. "Edge classification based on Convolutional Neural Networks for community detection in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    3. Chengdong Zhang & Keke Li & Shaoqing Wang & Bin Zhou & Lei Wang & Fuzhen Sun, 2023. "Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link Prediction," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
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