Tensor Graph Convolutional Network for Dynamic Graph Representation
  Learning
Ling Wang Ye Yuan
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.