解决GNN深层网络过平滑问题，实现15层注意力网络无需调参
Deep Graph Attention Networks
- 通过保持不同类别节点在每层的差异性，避免过平滑
- 15层DeepGAT性能媲美2层GAT，注意力系数相似
- 无需调整层数，适合构建大规模高效图神经网络
图可用于表示各类现实世界对象。然而，图神经网络（GNN）容易出现过平滑问题，即随着层数增加，不同类别节点的表示趋于相似，导致性能下降。为有效构建图注意力网络（GAT），需一种无需反复调试层数的方法。为此，本文提出DeepGAT，用于深层GAT中节点分类预测。该方法通过确保不同类别节点在每层不相似，避免过平滑。使用DeepGAT可构建15层网络而无需调参。实验表明，15层DeepGAT的性能与2层GAT相当，注意力系数相近。DeepGAT使大型网络获得与浅层网络相似的注意力分布，解决了过平滑问题，无需调参，节省时间并提升性能。
原文摘要 · Abstract (English)
Graphs are useful for representing various realworld objects. However, graph neural networks (GNNs) tend to suffer from over-smoothing, where the representations of nodes of different classes become similar as the number of layers increases, leading to performance degradation. A method that does not require protracted tuning of the number of layers is needed to effectively construct a graph attention network (GAT), a type of GNN. Therefore, we introduce a method called "DeepGAT" for predicting the class to which nodes belong in a deep GAT. It avoids over-smoothing in a GAT by ensuring that nodes in different classes are not similar at each layer. Using DeepGAT to predict class labels, a 15-layer network is constructed without the need to tune the number of layers. DeepGAT prevented over-smoothing and achieved a 15-layer GAT with similar performance to a 2-layer GAT, as indicated by the similar attention coefficients. DeepGAT enables the training of a large network to acquire similar attention coefficients to a network with few layers. It avoids the over-smoothing problem and obviates the need to tune the number of layers, thus saving time and enhancing GNN performance.
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