Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view interpret various GCNs guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which building appropriate regularizers can m...

Inspired by the recent success of self-supervised methods applied on images, learning graph structured data has seen rapid growth especially centered augmentation-based contrastive methods. However, we argue that without carefully designed augmentation techniques, augmentations graphs may behave arbitrarily in underlying semantics can drastically change. As a consequence, performance existing i...

For p∈N, a coloring λ of the vertices graph G is p-centered if for every connected subgraph H G, either receives more than p colors under or there color that appears exactly once in H. Centered colorings play an important role theory sparse classes introduced by Nešetřil and Ossona de Mendez [31], [32], as they structurally characterize bounded expansion — one key sparsity notions this theory. ...