Attention-based graph neural networks: a survey
Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topological structures. In recent years, attention mechanism, which is brilliant in the fields of natural language processing and computer vision, is introduced to GNNs to adaptively select the discriminative features and automatically filter the noisy information. To the best of our knowledge, due to the fast-paced advances in this domain, a systematic overview of attention-based GNNs is still missing. To fill this gap, this paper aims to provide a comprehensive survey on recent advances in attention-based GNNs. Firstly, we propose a novel two-level ...
| Authors | Chengcheng Sun, Chenhao Li, Xiang Lin et al. |
|---|---|
| Primary category | cs.SI |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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