Higher-order Weighted Graph Convolutional Networks
Graph Convolution Network has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power.In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors.Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations.Our model, called higher-order weighted GCN, has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.
We propose HWGCN to mix the relevant neighborhood information at different orders to better learn node representations.The authors propose a variant of GCN, HWGCN, to consider convolution beyond 1-step neighbors, which is comparable to state-of-the-art methods.