​​Deep learning for dynamic graphs: models and benchmarks

Guest post by Alessio Gravina

Published in IEEE Transactions on Neural Networks and Learning Systems
📜 arxiv preprint: link
🛠️ code: GitHub

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 real world systems of interconnected entities, which evolve over time.

In light of this, in this paper we proposed, at first, a survey that focuses on recent representation learning techniques for dynamic graphs under a uniform formalism consolidated from existing literature. Second, we provide the research community with a fair performance comparison among the most popular methods of the three families of dynamic graph problems, by leveraging a reproducible experimental environment.

We believe that this work will help fostering the research in the domain of dynamic graphs by providing a clear picture of the current development status and a good baseline to test new architectures and approaches.