The Transformer architecture has recently gained considerable attention in
the field of graph representation learning, as it naturally overcomes several
limitations of Graph Neural Networks (GNNs) with customized attention
mechanisms or positional and structural encodings. Despite making some
progress, existing works tend to overlook external information of graphs,
specifically the correlation between graphs. Intuitively, graphs with similar
structures should have similar representations. Therefore, we propose Graph
External Attention (GEA) -- a novel attention mechanism that leverages multiple
external node/edge key-value units to capture inter-graph correlations
implicitly. On this basis, we design an effective architecture called Graph
External Attention Enhanced Transformer (GEAET), which integrates local
structure and global interaction information for more comprehensive graph
representations. Extensive experiments on benchmark datasets demonstrate that
GEAET achieves state-of-the-art empirical performance. The source code is
available for reproducibility at: this https URL

Transformer 的图表示学习中，通过引入 Graph External Attention 提出了一种新的关注机制来隐式捕捉图之间的相关性，进而用于设计了更全面的图表示框架 GEAET。通过在基准数据集上进行的实验证明，GEAET 达到了最先进的经验性能。