  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.
