Benchmarking and Analysis for Graph Neural Network Node Classification Task
In contrast with previous graph embedding algorithms,the graph neural network model performs tasks such as node classification more effectively because it can better coordinate the learning of hidden node features with the classification target due to its end-to-end model architecture in the training process.However,the experimental comparison stage of existing graph neural models frequently suffers from problems such as specific types of experimental datasets,insufficient dataset sample size,ir-regular splitting of the train and test sets,limited scale and scope of comparison models,homogeneous performance evaluation metrics,and lack of comparative analysis for model's training time consumption.To this end,in order to provide decision guide-lines for GNN model selection in real business scenarios,a total of 20 datasets from various domains(citation networks,social net-works,collaboration networks,etc.),including cora,citeseer,pubmed,deezer,etc.,are chosen to conduct a comprehensive and equitable benchmark evaluation of node classification tasks on 17 mainstream graph neural network models,including FastGCN,PPNP,ChebyNet,DAGNN,etc.,on performance evaluation metrics including accuracy,precision,recall,F-score value,and model training time.The benchmarking experiments revealed that,on the one hand,the factors that affect the speed of model training are node attribute dimension,graph node size and graph edge size in turn;on the other hand,there is no winner-take-all model,that is,there is no model that performs well across all benchmark datasets,especially in a fair benchmarking configuration,the model with simple structure has better performance than the complex GNN models.
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