Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

• URL: http://arxiv.org/abs/2401.07065v1
• Date: Sat, 13 Jan 2024 12:49:56 GMT
• Title: Tensor Graph Convolutional Network for Dynamic Graph Representation Learning
• Authors: Ling Wang, Ye Yuan
• Abstract summary: Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network. We propose a tensor graph convolutional network to learn DG representations in one convolution framework.
• Score: 12.884025972321316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form; b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance.

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