Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

First, explosive increase of graph data poses a great challenge to GNN training on large-scale datasets. Previously, Graph neural networks (GNNs) have been a hot many graph-based tasks were often conducted on toy datasets spot of recent research and are widely utilized in diverse that are relatively small compared to graphs in realistic applications, applications. However, with the use of huger which is harmful to model scalability and practical data and deeper models, an urgent demand is unsurprisingly usages. Currently, large-scale graph datasets are thereby proposed made to accelerate GNNs for more efficient in literature [Hu et al., 2020a] for advanced research, execution. In this paper, we provide a comprehensive and at the same time, making GNNs execution (i.e., training survey on acceleration methods for GNNs and inference) a time-consuming process.