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Graph neural networks (GNNs) have achieved outstanding performance inlearning graph-structured data and various tasks. However, many current GNNssuffer from three common problems when facing large-size graphs or using adeeper structure: neighbors explosion, node dependence, and oversmoothing. Suchproblems attribute to the data structures of the graph itself or the designingof the multi-layers GNNs framework, and can lead to low training efficiency andhigh space complexity. To deal with these problems, in this paper, we propose ageneral subgraph-based training framework, namely Ripple Walk Training (RWT),for deep and large graph neural networks. RWT samples subgraphs from the fullgraph to constitute a mini-batch, and the full GNN is updated based on themini-batch gradient. We analyze the high-quality subgraphs to train GNNs in atheoretical way. A novel sampling method Ripple Walk Sampler works for samplingthese high-quality subgraphs to constitute the mini-batch, which considers boththe randomness and connectivity of the graph-structured data. Extensiveexperiments on different sizes of graphs demonstrate the effectiveness andefficiency of RWT in training various GNNs (GCN & GAT).

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