Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features.
翻译:虽然图表代表性学习(GRL)取得了显著进展,但以适当的方式提取和嵌入丰富的地形结构和特征信息仍是一项挑战。大多数现有方法侧重于地方结构,未能充分纳入全球地形结构。为此,我们提议采用新的结构-保留图表代表性学习(SPGRL)方法,以充分捕捉图的结构信息。具体地说,为减少原始图的不确定性和误差,我们通过 k- 远邻邻方法构建了一个特征图,作为补充性视图。该特征图可用于在节点一级对比,以捕捉当地关系。此外,我们保留全球地形结构信息,办法是最大限度地利用整个图表和特征嵌入的相互信息(MI),从理论上说,这种信息已缩减为交换特征嵌入的特征和原始图以进行自我重建。广泛的实验表明,我们的方法在半监督节点分类任务上表现优异,在图形结构或节点特征的噪音渗透下非常稳健。