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Non-Local Graph Neural Network - 2021

Research Area:  Machine Learning


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.

Author(s) Name:  Meng Liu; Zhengyang Wang; Shuiwang Ji

Journal name:  IEEE Transactions on Pattern Analysis and Machine Intelligence

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TPAMI.2021.3134200

Volume Information:  Page(s): 1 - 1