Abstract: Graph neural network is the mainstream tool of graph machine learning at the current stage,and it has broad development prospects.By constructing an abstract graph structure,the graph neural network model can be used to efficiently deal with problems in various application scenarios,including node prediction,link prediction,and graph classification.But the application on large-scale graphs has always been the key point and difficulty in graph neural network training.And how to effectively and quickly train and deploy graph neural networks on large-scale graph data is a major problem hindering the further industrial application of graph neural networks.Graph neural network can use the topological information of the network structure of the graph,so as to achieve better results than other general neural networks such as multi-layer perceptron on the node prediction problem.But the rapid growth of the number of nodes and edges of the graph's network structure restricts the training of the graph neural network,and the number of nodes in the real dataset is tens of millions or even billions,or the number of edges in some dense network structures has reached tens of millions.This makes it difficult for traditional graph neural network training methods to achieve direct results.This paper improves and proposes a new framework for graph neural network training based on heuristic graph coarsening algorithms,and proposes two specific training algorithms on this basis.Then this paper proposes two simple heuristic graph coarsening algorithms.Under the guarantee that the loss of accuracy is acceptable and the memory space consumption is greatly reduced,the proposed algorithm can further significantly reduce both calculation time and training time of graph neural networks.Experiment shows that satisfactory results can be achieved on common datasets.

Key words: Graph neural network, Graph coarsening, Training acceleration, Heuristic, Random walk, Unbiased