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Non-Local Graph Neural Network - 2021

Research Area:  Machine Learning

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.

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