Variants of Graph Neural Networks (GNNs) for representation learning have been proposed recently and achieved fruitful results in various fields. Among them, graph attention networks (GATs) first employ a self-attention strategy to learn attention weights for each edge in the spatial domain. However, learning the attentions over edges only pays attention to the local information of graphs and greatly increases the number of parameters. In this paper, we first introduce attentions in the spectral domain of graphs. Accordingly, we present Spectral Graph Attention Network (SpGAT) that learn representations for different frequency components regarding weighted filters and graph wavelets bases. In this way, SpGAT can better capture global patterns of graphs in an efficient manner with much fewer learned parameters than that of GAT. We thoroughly evaluate the performance of SpGAT in the semi-supervised node classification task and verified the effectiveness of the learned attentions in the spectral domain.

中文翻译：

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谱图注意力网络

最近提出了用于表示学习的图神经网络 (GNN) 的变体，并在各个领域取得了丰硕的成果。其中，图注意力网络（GAT）首先采用自注意力策略来学习空间域中每条边的注意力权重。然而，学习边上的注意力只关注图的局部信息，大大增加了参数的数量。在本文中，我们首先介绍了图谱域中的注意力。因此，我们提出了频谱图注意力网络（SpGAT），它学习关于加权滤波器和图小波基的不同频率分量的表示。通过这种方式，SpGAT 可以以比 GAT 少得多的学习参数，以有效的方式更好地捕获图的全局模式。