Non-Local Graph Neural Networks

Meng Liu, Zhengyang Wang, Shuiwang Ji

Keywords: Graph Neural Networks, Non-Local Aggregation, Attention, Disassortative Graph

Abstract: Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortative graphs. In this work, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. Based on it, we develop various non-local GNNs. We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs. Experimental results demonstrate that our non-local GNNs significantly outperform previous state-of-the-art methods on six benchmark datasets of disassortative graphs, in terms of both model performance and efficiency.

One-sentence Summary: A simple yet effective non-local aggregator for capturing long-range information on disassortative graphs.

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