TREND: TempoRal Event and Node Dynamics for Graph Representation Learning

被引:31
|
作者
Wen, Zhihao [1 ]
Fang, Yuan [1 ]
机构
[1] Singapore Management Univ, Singapore, Singapore
关键词
Temporal graphs; Hawkes process; GNN; event and node dynamics;
D O I
10.1145/3485447.3512164
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects which is the ability of events to influence the occurrence of another event. In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). TREND presents a few major advantages: (1) it is inductive due to its GNN architecture; (2) it captures the exciting effects between events by the adoption of the Hawkes process; (3) as our main novelty, it captures the individual and collective characteristics of events by integrating both event and node dynamics, driving a more precise modeling of the temporal process. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed model.
引用
收藏
页码:1159 / 1169
页数:11
相关论文
共 50 条
  • [1] Event-Based Dynamic Graph Representation Learning for Patent Application Trend Prediction
    Zou, Tao
    Yu, Le
    Sun, Leilei
    Du, Bowen
    Wang, Deqing
    Zhuang, Fuzhen
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 36 (05) : 1951 - 1963
  • [2] Property graph representation learning for node classification
    Shu Li
    Nayyar A. Zaidi
    Meijie Du
    Zhou Zhou
    Hongfei Zhang
    Gang Li
    [J]. Knowledge and Information Systems, 2024, 66 (1) : 237 - 265
  • [3] Property graph representation learning for node classification
    Li, Shu
    Zaidi, Nayyar A.
    Du, Meijie
    Zhou, Zhou
    Zhang, Hongfei
    Li, Gang
    [J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2024, 66 (01) : 237 - 265
  • [4] Graph Representation Learning Beyond Node and Homophily
    Li, You
    Lin, Bei
    Luo, Binli
    Gui, Ning
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (05) : 4880 - 4893
  • [5] Automated Graph Representation Learning for Node Classification
    Sun, Junwei
    Wang, Bai
    Wu, Bin
    [J]. 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [6] A dynamic graph representation learning based on temporal graph transformer
    Zhong, Ying
    Huang, Chenze
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2023, 63 : 359 - 369
  • [7] A dynamic graph representation learning based on temporal graph transformer
    Zhong, Ying
    Huang, Chenze
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2023, 63 : 359 - 369
  • [8] Node Information Awareness Pooling for Graph Representation Learning
    Sun, Chuan
    Huang, Feihu
    Peng, Jian
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2022, PT I, 2022, 13280 : 182 - 193
  • [9] Node representation learning with graph augmentation for sequential recommendation
    Zhu, Yingzheng
    Liang, Xiufang
    Duan, Huajuan
    Xu, Fuyong
    Wang, Yuanying
    Liu, Peiyu
    Lu, Ran
    [J]. INFORMATION SCIENCES, 2023, 646
  • [10] Multiscale Representation Learning of Graph Data With Node Affinity
    Gao, Xing
    Dai, Wenrui
    Li, Chenglin
    Xiong, Hongkai
    Frossard, Pascal
    [J]. IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, 2021, 7 : 30 - 44