Predicting Path Failure In Time-Evolving Graphs
This work addresses path failure prediction in dynamic networks, which is crucial for applications like telecommunication and traffic management, but it appears incremental as it builds on existing graph and temporal modeling techniques.
The paper tackles the problem of predicting path failure in time-evolving graphs, such as telecommunication and traffic networks, by proposing a novel deep neural network called LRGCN and a path representation method named SAPE, achieving superior performance in experiments on real-world datasets.
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.