# Predicting Path Failure In Time-Evolving Graphs

## TL;DR

This paper introduces LRGCN, a deep neural network that models temporal and structural dynamics in time-evolving graphs to predict path failures, with applications in telecommunication and traffic networks.

## Contribution

It proposes LRGCN, a novel deep learning model combining relational GCN and LSTM for dynamic graph analysis, and introduces SAPE for fixed-length path embedding.

## Key findings

- LRGCN outperforms existing methods in path failure prediction.
- SAPE effectively embeds arbitrary-length paths into fixed vectors.
- Experiments validate the model's superiority on real-world networks.

## Abstract

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.