In this paper we use a time-evolving graph which consists of a sequence of
graph snapshots over time to model many real-world networks. We study the path
classification problem in a time-evolving graph, which has many applications in
real-world scenarios, for example, predicting path failure in a
telecommunication network and predicting path congestion in a traffic network
in the near future. In order to capture the temporal dependency and graph
structure dynamics, we design a novel deep neural network named Long Short-Term
Memory R-GCN (LRGCN). LRGCN considers temporal dependency between time-adjacent
graph snapshots as a special relation with memory, and uses relational GCN to
jointly process both intra-time and inter-time relations. We also propose a new
path representation method named self-attentive path embedding (SAPE), to embed
paths of arbitrary length into fixed-length vectors. Through experiments on a
real-world telecommunication network and a traffic network in California, we
demonstrate the superiority of LRGCN to other competing methods in path failure
prediction, and prove the effectiveness of SAPE on path representation.

本篇论文介绍了用时间演化图对现实中的网络进行建模，并研究了时间演化图中路径分类问题的应用，设计了一种新颖的深度神经网络 LRGCN 和路径表示方法 SAPE，实验结果表明 LRGCN 在路径预测方面比其他竞争方法更有效，SAPE 在路径表示方面也表现出显著的效果。