Adaptive-propagating heterophilous graph convolutional network

Abstract

Graph convolutional networks have significant advantages in dealing with graph-structured data, but most existing methods usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real-world graph-structured data. Due to the propagation mechanism of graph convolutional networks, it is challenging to prevent the interference aggregation from nodes of different classes, which may result in the incorporation of noise and irrelevant data in the outcome, ultimately decreasing the performance of the model. In this paper, we propose a new framework to address this issue on heterophilous graph-structured data. The proposed method comprises two main components. On one hand, the homophily of the graph-structured data is modeled so that the method can adaptively adjust the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information. On the other hand, the implicit node interaction is captured through the learned feature space, which is then fused with the original interaction to aggregate sufficient intra-class knowledge. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method against current state-of-the-art approaches. 22Code to reproduce our experiments is available at https://github.com/rebridger/APHGCN.