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 convolutional methods, this method learns subgraphs which have a star topology rather than a fixed graph. 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. As for CNNs in Euclidean feature space, the convolutional filter is localized and maintains a good weight sharing property. By introducing deep layers, the method can learn global features like a CNN. To validate the method, STC was compared to state-of-the-art spectral convolutional and spatial convolutional methods in a supervised learning setting on three benchmark datasets: Cora, Citeseer and Pubmed. The experimental results show that STC outperforms the other methods. STC was also applied to protein identification tasks and outperformed traditional and advanced protein identification methods.