Revisiting Deformable Convolution on Graphs: Large-Range Modeling and Robustness.

Abstract

Graph Convolution Networks (GCNs) have achieved remarkable success in representation of structured graph data. As we know that traditional GCNs are generally defined on the fixed first-order neighborhood receptive field which makes them be incapable to capture the long-range dependencies between distant nodes and also vulnerable to graph attacks and noises. To address these limitations, we revisit deformable convolution on graphs and propose a novel deformable graph convolution, termed Neighborhood-Deformable Graph Convolution (NDGC). The core of NDGC is to explicitly achieve the deformable convolution on graphs by introducing virtual neighbors which encode large-range information via the offsetting and interpolation function. That is, the introduced virtual neighbors can provide a larger receptive field with deformable receptive shape for graph convolution definition. Also, NDGC conducts message aggregation on the deformable virtual neighbors which thus performs more robustly w.r.t. graph attacks and noises. In particular, NDGC provides a general neighborhood deformable scheme, seamlessly integrating with many graph convolution definitions to derive their deformable variants. Experimental results validate the effectiveness and advantages of the proposed NDGC networks on several graph learning tasks.