基于时空建模的动态图卷积神经网络

为了使图表示学习得到的嵌入向量对节点和边不断变化的动态图具有很好的信息表征能力, 提出一种动态图卷积神经网络模型(DyGCN), 将动态图上的表示学习建模为时间和空间信息的聚合。该模型将从图卷积神经网络(GCN)的空间卷积提取图上的结构信息与从时间卷积神经网络(TCN)的因果卷积提取时序上的历史信息相结合, 同时在空间卷积层加入自适应的模型更新机制, 使得模型参数随着图结构的变化能够自适应地更新。在金融领域数据集上针对金融欺诈检测进行的边分类实验表明, 该模型比现有方法有很大的性能提升。

A Dynamic Graph Convolutional Network Based on Spatial-Temporal Modeling

In order to learn high-level representation with rich information for dynamic graphs where nodes and edges change dynamically, a dynamic graph convolutional network (DyGCN) is proposed to learn representation as a mixture of both spatial and temporal information. The model performs spatial convolutions to learn structural information on graphs and temporal convolutions to learn historical information along time axis. Besides, the selfadapting mechanism on the spatial convolution layer allows model parameters to update with graphs. Extensive experiments on financial networks for edge classification tasks against financial crimes show that DyGCN outperforms state-of-the-art methods.