Dynamic Graph Representation Learning with Neural Networks: A Survey

TL;DR

This survey reviews recent advances in dynamic graph neural networks, highlighting their ability to model temporal and topological data for various applications, and providing guidelines for designing effective DGNNs.

Contribution

It offers a comprehensive overview of dynamic graph learning models, analyzing their approaches to temporal modeling and proposing design guidelines for DGNNs.

Findings

Dynamic graph learning models vary in how they incorporate time information.

DGNNs are the current state-of-the-art for dynamic graph tasks.

The survey identifies key challenges and future directions in the field.

Abstract

In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs allow to efficiently handle applications such as social network prediction, recommender systems, traffic forecasting or electroencephalography analysis, that can not be adressed using standard numeric representations. As a direct consequence of the emergence of dynamic graph representations, dynamic graph learning has emerged as a new machine learning problem, combining challenges from both sequential/temporal data processing and static graph learning. In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years. This paper aims at providing a review of problems and models related to dynamic graph learning. The various dynamic graph supervised learning settings are analysed and discussed. We identify the similarities and differences between existing models with respect to the way time information is modeled. Finally, general guidelines for a DGNN designer when faced with a dynamic graph learning problem are provided.