Deep learning for dynamic graphs: models and benchmarks

arXiv:2307.06104v3 Announce Type: replace
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 …

abstract arxiv benchmarks challenges cs.lg cs.si deep learning domain dynamic graph graphs growth making networks progress research type unsolved