Deep learning for dynamic graphs: models and benchmarks

TL;DR

This paper surveys recent advances in deep learning models for dynamic graphs, compares their performance on key tasks, and provides a baseline for future research in evolving interconnected systems.

Contribution

It offers a comprehensive overview of state-of-the-art dynamic graph learning methods and conducts a rigorous performance comparison to establish benchmarks.

Findings

Deep models effectively learn temporal and spatial information in dynamic graphs.

Performance comparison highlights strengths and weaknesses of current approaches.

Provides a baseline for future research and model evaluation.

Abstract

Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved. Specifically, there is an urge of making DGNs suitable for predictive tasks on realworld systems of interconnected entities, which evolve over time. With the aim of fostering research in the domain of dynamic graphs, at first, we survey recent advantages in learning both temporal and spatial information, providing a comprehensive overview of the current state-of-the-art in the domain of representation learning for dynamic graphs. Secondly, we conduct a fair performance comparison among the most popular proposed approaches on node and edge-level tasks, leveraging rigorous model selection and assessment for all the methods, thus establishing a sound baseline for evaluating new architectures and approaches