Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

2024/01/13 by Ye Yuan, Wang, Ling, Yuan, Ye
Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2401.07065

openalex publication_date 2024/01/13 · openalex created_date 2024/01/18 · openalex updated_date 2026/07/28

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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