Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link Prediction

被引:1
|
作者
Zhang, Chengdong [1 ]
Li, Keke [1 ]
Wang, Shaoqing [1 ]
Zhou, Bin [1 ]
Wang, Lei [1 ]
Sun, Fuzhen [1 ]
机构
[1] Shandong Univ Technol, Sch Comp Sci & Technol, Zibo 255091, Peoples R China
关键词
graph neural network; heterogeneous graph; metapath; link prediction;
D O I
10.3390/math11030578
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Along with the growth of graph neural networks (GNNs), many researchers have adopted metapath-based GNNs to handle complex heterogeneous graph embedding. The conventional definition of a metapath only distinguishes whether there is a connection between nodes in the network schema, where the type of edge is ignored. This leads to inaccurate node representation and subsequently results in suboptimal prediction performance. In heterogeneous graphs, a node can be connected by multiple types of edges. In fact, each type of edge represents one kind of scene. The intuition is that if the embedding of nodes is trained under different scenes, the complete representation of nodes can be obtained by organically combining them. In this paper, we propose a novel definition of a metapath whereby the edge type, i.e., the relation between nodes, is integrated into it. A heterogeneous graph can be considered as the compound of multiple relation subgraphs from the view of a novel metapath. In different subgraphs, the embeddings of a node are separately trained by encoding and aggregating the neighbors of the intrapaths, which are the instance levels of a novel metapath. Then, the final embedding of the node is obtained by the use of the attention mechanism which aggregates nodes from the interpaths, which is the semantic level of the novel metapaths. Link prediction is a downstream task by which to evaluate the effectiveness of the learned embeddings. We conduct extensive experiments on three real-world heterogeneous graph datasets for link prediction. The empirical results show that the proposed model outperforms the state-of-the-art baselines; in particular, when comparing it to the best baseline, the F1 metric is increased by 10.35% over an Alibaba dataset.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] k-Hopped Link Prediction With Graph Embedding
    Jui, Tonni Das
    Baker, Erich
    Benton, Mary Lauren
    [J]. Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023, 2023, : 600 - 607
  • [42] Open Knowledge Graph Link Prediction with Segmented Embedding
    Xie, Tingyu
    Peng, Peng
    Wang, Hongwei
    Liu, Yusheng
    [J]. 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022,
  • [43] A Hierarchical Knowledge Graph Embedding Framework for Link Prediction
    Liu, Shuang
    Hou, Chengwang
    Meng, Jiana
    Chen, Peng
    Kolmanic, Simon
    [J]. IEEE Access, 2024, 12 : 173338 - 173350
  • [44] Scaling Knowledge Graph Embedding Models for Link Prediction
    Sheikh, Nasrullah
    Qin, Xiao
    Reinwald, Berthold
    Lei, Chuan
    [J]. PROCEEDINGS OF THE 2022 2ND EUROPEAN WORKSHOP ON MACHINE LEARNING AND SYSTEMS (EUROMLSYS '22), 2022, : 87 - 94
  • [45] DEEP WEIGHTED GRAPH EMBEDDING FOR LINK WEIGHT PREDICTION
    Zuo Wenbo
    Liu Zhen
    [J]. 2022 19TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2022,
  • [46] Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders
    Keser, Reyhan Kevser
    Nallbani, Indrit
    Calik, Nurullah
    Ayanzadeh, Aydin
    Toreyin, Behcet Ugur
    [J]. 2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
  • [47] Discriminative Path-Based Knowledge Graph Embedding for Precise Link Prediction
    Zhang, Maoyuan
    Wang, Qi
    Xu, Wukui
    Li, Wei
    Sun, Shuyuan
    [J]. ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018), 2018, 10772 : 276 - 288
  • [48] Systematic Biases in Link Prediction: Comparing Heuristic and Graph Embedding Based Methods
    Sinha, Aakash
    Cazabet, Remy
    Vaudaine, Remi
    [J]. COMPLEX NETWORKS AND THEIR APPLICATIONS VII, VOL 1, 2019, 812 : 81 - 93
  • [49] UAV ad hoc network link prediction based on deep graph embedding
    Shu J.
    Wang Q.
    Liu L.
    [J]. Tongxin Xuebao/Journal on Communications, 2021, 42 (07): : 137 - 149
  • [50] Link Prediction Based on Contrastive Multiple Heterogeneous Graph Convolutional Networks
    Chen, Dongming
    Shen, Yue
    Chen, Huilin
    Nie, Mingshuo
    Wang, Dongqi
    [J]. ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT XIII, ICIC 2024, 2024, 14874 : 334 - 345