论文标题
定向图自动编码器
Directed Graph Auto-Encoders
论文作者
论文摘要
我们为有向图引入了一类新的自动编码器，这是由Weisfeiler-Leman算法直接扩展到成对节点标签的动机。提出的模型学习有向图的节点的一对可解释的潜在表示，并使用参数化图形卷积网络（GCN）层进行编码器和不对称的内部产品解码器。编码器中的参数控制相邻节点之间交换的表示的加权。我们演示了所提出的模型学习有意义的潜在嵌入并在几个流行网络数据集上的有向链接预测任务上实现卓越性能的能力。
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses parameterized graph convolutional network (GCN) layers for its encoder and an asymmetric inner product decoder. Parameters in the encoder control the weighting of representations exchanged between neighboring nodes. We demonstrate the ability of the proposed model to learn meaningful latent embeddings and achieve superior performance on the directed link prediction task on several popular network datasets.
