Graph Convolutional Networks (GCN) and their variants have achieved brilliant results in graph representation learning. However, most existing methods cannot be utilized for deep architectures and can only capture the low order proximity in networks. In this paper, we have proposed a Residual Simple Graph Convolutional Network (RSGCN), which can aggregate information from distant neighbor node features without over-smoothing and vanishing gradients. Given that node features of the same class have certain similarity, a weighted feature propagation is considered to ensure effective information a...
