A novel message passing neural network based on neighborhood expansion

Abstract

Most message passing neural networks (MPNNs) are widely used for assortative network representation learning under the assumption of homophily between connected nodes. However, this fundamental assumption is inconsistent with the heterophily of disassortative networks (DNs) in many real-world applications. Therefore, we propose a novel MPNN called NEDA based on neighborhood expansion for disassortative network representation learning (DNRL). Specifically, our NEDA first performs neighborhood expansion to seek more informative nodes for aggregation and then performs data augmentation to speed up the optimization process of a set of parameter matrices at the maximum available training data with minimal computational cost. To evaluate the performance of NEDA comprehensively, we perform several experiments on benchmark disassortative network datasets with variable sizes, where the results demonstrate the effectiveness of our NEDA model. The code is publicly available at https://github.com/xueyanfeng/NEDA.