Efficient and Effective Implicit Dynamic Graph Neural Network

arXiv:2406.17894v1 Announce Type: new
Abstract: Implicit graph neural networks have gained popularity in recent years as they capture long-range dependencies while improving predictive performance in static graphs. Despite the tussle between performance degradation due to the oversmoothing of learned embeddings and long-range dependency being more pronounced in dynamic graphs, as features are aggregated both across neighborhood and time, no prior work has proposed an implicit graph neural model in a dynamic setting. In this paper, we present Implicit Dynamic Graph …

abstract arxiv cs.lg dependencies dynamic embeddings features graph graph neural network graph neural networks graphs improving network networks neural network neural networks performance predictive type while