Tensor graph convolutional neural network
This work addresses efficient graph convolution for specific graph types like dynamic and cross-attribute graphs, which is incremental as it builds on existing graph neural network methods.
The authors tackled the problem of performing convolution on factorizable graphs, such as sequential dynamic graphs and cross-attribute graphs, by proposing a tensor graph convolutional neural network (TGCNN) that reduces memory and computational burden. The method achieved competitive performance with state-of-the-art methods on skeleton action and matrix completion datasets.
In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs. Especially, we propose a graph preserving layer to memorize salient nodes of those factorized subgraphs, i.e. cross graph convolution and graph pooling. For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs. Taking this operation, then general graph convolution may be efficiently performed followed by the composition of small matrices, which thus reduces high memory and computational burden. Encapsuling sequence graphs into a recursive learning, the dynamics of graphs can be efficiently encoded as well as the spatial layout of graphs. To validate the proposed TGCNN, experiments are conducted on skeleton action datasets as well as matrix completion dataset. The experiment results demonstrate that our method can achieve more competitive performance with the state-of-the-art methods.