Before revision: The performances of GNNs for representation learning on the graph-structured data are generally limited to the issue that existing GNNs rely on one assumption, i. e., the original graph structure is reliable. However, since real-world graphs is inevitably noisy or incomplete, this assumption is often unrealistic. In this paper, we propose a structure learning graph convolutional networks (SLGCNs)to alleviate the issue from two aspects, and the proposed approach is applied to node classification.
After revision: The performances of GNNs for representation learning on the graph-structured data , under heterophily (or low homophily), many popular GNNs may fail to capture long-range dependencies, which leads to their performance degradation. To solve the above-mentioned issue, we propose a structure learning graph convolutional networks (SLGCNs)to alleviate the issue from two aspects, and the proposed approach is applied to node classification.