HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

被引:2
|
作者
Zhong, Hongwei [1 ]
Wang, Mingyang [1 ]
Zhang, Xinyue [1 ]
机构
[1] Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Peoples R China
基金
中国国家自然科学基金;
关键词
heterogeneous network; network embedding; metapath; graph neural network;
D O I
10.3390/electronics12092124
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Heterogeneous Graph Neural Network with Hypernetworks for Knowledge Graph Embedding
    Liu, Xiyang
    Zhu, Tong
    Tan, Huobin
    Zhang, Richong
    [J]. SEMANTIC WEB - ISWC 2022, 2022, 13489 : 284 - 302
  • [2] Semantic-guided graph neural network for heterogeneous graph embedding
    Han, Mingjing
    Zhang, Han
    Li, Wei
    Yin, Yanbin
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 232
  • [3] MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
    Fu, Xinyu
    Zhang, Jiani
    Men, Ziqiao
    King, Irwin
    [J]. WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), 2020, : 2331 - 2341
  • [4] PHGNN: Position-aware Graph Neural Network for Heterogeneous Graph Embedding
    Yang, Hangjun
    Li, Linsen
    Zhang, Lingxuan
    Tang, Junhua
    Chen, Zhongwei
    [J]. 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [5] MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs
    Ni, Mingjian
    Song, Yinghao
    Wang, Gongju
    Feng, Lanxiao
    Li, Yang
    Yan, Long
    Li, Dazhong
    Wang, Yanfei
    Zhang, Shikun
    Song, Yulun
    [J]. EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 2023, 10 (06):
  • [6] Embedding-Based Deep Neural Network and Convolutional Neural Network Graph Classifiers
    Elnaggar, Sarah G.
    Elsemman, Ibrahim E.
    Soliman, Taysir Hassan A.
    [J]. ELECTRONICS, 2023, 12 (12)
  • [7] Heterogeneous Graph Neural Network
    Zhang, Chuxu
    Song, Dongjin
    Huang, Chao
    Swami, Ananthram
    Chawla, Nitesh V.
    [J]. KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 793 - 803
  • [8] Local Structural Aware Heterogeneous Information Network Embedding Based on Relational Self-Attention Graph Neural Network
    Cao, Meng
    Yuan, Jinliang
    Xu, Ming
    Yu, Hualei
    Wang, Chongjun
    [J]. IEEE ACCESS, 2021, 9 : 88301 - 88312
  • [9] Status-Aware Signed Heterogeneous Network Embedding With Graph Neural Networks
    Lin, Wanyu
    Li, Baochun
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (04) : 4580 - 4592
  • [10] Deep heterogeneous network embedding based on Siamese Neural Networks
    Zhang, Chen
    Tang, Zhouhua
    Yu, Bin
    Xie, Yu
    Pan, Ke
    [J]. NEUROCOMPUTING, 2020, 388 : 1 - 11