A systematic analysis and guidelines of graph neural networks for practical applications

•We analyze the state-of-the-art methods of graph neural networks (GNN) in four phases.•1300 combinations of methods are compared with 13 well-known benchmark datasets.•It results in five guidelines to use GNN for practical graph-related problems.•Comparative experiments with more than 3600 runs support the analysis and guidelines.•Experimental reproducibility and replicability are verified by comparing with the literature.