Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which Graph Convolutional Network (GCN) has emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In this work, we propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN), which is an extension of GCN-based methods. We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings. The most affected nodes are first updated, and then their changes are propagated to the further nodes and leads to their update. Extensive experiments conducted on various dynamic graphs demonstrate that our model can update the node embeddings in a time-saving and performance-preserving way.
翻译:图像嵌入旨在学习节点的低维表示(aka. 嵌入),最近受到高度重视。近些年来,静态图上的努力激增,其中图变网络(GCN)已成为有效的模型。但是,这些方法主要侧重于静态图嵌入。在这项工作中,我们提出了一个高效的动态图嵌入方法,即动态图变化网络(DyGCN),这是以GCN为基础的方法的延伸。我们自然地将GCN嵌入的传播方案推广到一个高效的动态设置,即沿着图表传播变化以更新节点。最受影响的节点首先更新,然后将其变化传播到进一步的节点,并导致其更新。在各种动态图上进行的广泛实验表明,我们的模型可以以节省时间和保持性能的方式更新节点嵌入。