Related papers: Non-Local Graph Neural Networks

Non-Local Graph Neural Networks

URL: http://arxiv.org/abs/2005.14612v2
Date: Sat, 11 Dec 2021 01:17:40 GMT
Title: Non-Local Graph Neural Networks
Authors: Meng Liu, Zhengyang Wang, Shuiwang Ji
Abstract summary: We propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs.
Score: 60.28057802327858
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 seven benchmark datasets of disassortative graphs, in terms of both model performance and efficiency.

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