Star Topology Convolution for Graph Representation Learning
We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature space. Unlike most existing spectral methods, this method learns subgraphs which have a star topology rather than a fixed graph. Due to the good properties of a star topology, STC is graph/subgraph free. It has fewer parameters in its convolutional filter and is inductive so that it is more flexible and can be applied to large and evolving graphs. Similar to CNNs in Euclidean feature space, the convolutional filter is learnable and localized and maintains a good weight sharing property. To test the method, STC was compared to state-of-the-art spectral methods and spatial methods in a supervised learning setting on five benchmark datasets: Cora, Citeseer, Pubmed, Ogbn-Arxiv, and Ogbn-MAG. The experiment results show that STC outperforms other methods especially on large graphs. In an essential protein identification task, STC also outperforms state-of-the-art essential protein identification methods.