Hybrid deep graph convolutional networks.

Graph neural networks (GNNs) leverage graph convolutions or their approximations to cope with graph-structured data. According to whether convolution is applied to the spectral domain or spatial domain, advances in this direction are generally divided into spectral approaches and spatial approaches. However, current study generally utilizes one of the approaches, while ignoring the combination of the two approaches. In this paper, we propose hybrid deep graph convolutional networks (HDGCNs), novel neural network architectures that combine the spectral approach and the spatial approach to calculate the adjacency matrix, leveraging the deep graph convolutional networks (GCNs) to release the advantages of this combination. In this way, the gap between spectral approaches and spatial approaches is eliminated, and the characteristics of the two approaches are merged together. Extensive experiments on citation networks and web networks offer evidence that the proposed models outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]