Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network

Understand

Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data and various tasks.

• However, many current GNNs suffer from three common problems when facing large-size graphs or using a deeper structure: neighbors explosion, node dependence, and oversmoothing.
• Such problems attribute to the data structures of the graph itself or the designing of the multi-layers GNNs framework, and can lead to low training efficiency and high space complexity.
• To deal with these problems, in this paper, we propose a general subgraph-based training framework, namely Ripple Walk Training (RWT), for deep and large graph neural networks.