Topology Adaptive Graph Convolutional Networks

Understand

Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss.

• This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network defined in the vertex domain.
• We provide a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs.
• The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution.