论文标题
GTNET：基于树的深图学习体系结构
GTNet: A Tree-Based Deep Graph Learning Architecture
论文作者
论文摘要
我们提出了图形树网络（GTNETS），这是一种深层学习体系结构，具有新的通用消息传递方案，该方案源自图形的树表示。在树表示中，消息从叶子节点到根节点向上传播，并且每个节点在从其子节点（邻居）接收信息之前保留其初始信息。我们通过汇总其初始功能及其邻居节点的更新功能来制定一条通用传播规则，以传递给树中的消息的性质以更新节点的功能。在此GTNET体系结构 - 图树注意网络（GTAN）和图树卷积网络（GTCN）中提出了两个图表学习模型，并在几个流行的基准数据集中实验证明了最先进的性能。与具有“过度光滑”问题的Vanilla Graph注意力网络（GAT）和图形卷积网络（GCN）不同，所提出的GTAN和GTCN模型可以深入，如全面的实验和严格的理论分析所证明的那样。
We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to the root node, and each node preserves its initial information prior to receiving information from its child nodes (neighbors). We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features. Two graph representation learning models are proposed within this GTNet architecture - Graph Tree Attention Network (GTAN) and Graph Tree Convolution Network (GTCN), with experimentally demonstrated state-of-the-art performance on several popular benchmark datasets. Unlike the vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which have the "over-smoothing" issue, the proposed GTAN and GTCN models can go deep as demonstrated by comprehensive experiments and rigorous theoretical analysis.
