Acceleration Algorithms in GNNs: A Survey

arXiv:2405.04114v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up to real-world and large-scale graph applications. To address the critical challenges, a range of algorithms have been proposed to accelerate training and inference of GNNs, attracting increasing attention from the research community. In this paper, we present a systematic review of acceleration algorithms in GNNs, which can be categorized into three main …

algorithms arxiv cs.ai cs.lg gnns survey type