Multi-graph(MG)is a representation model of the bag-of-graphs,semi-supervised multi-graph classification aims to build a prediction model from marked and unmarked multi-graphs.Through high-accuracy prediction of unmarked multi-graphs,it's widely used in user product recommendation,biopharmaceuticals and other fields.There are two main shortcomings in the existing semi-supervised multi-graph classification based on machine learning:(1)It's cannot be fully automatic feature selection and relies too much on parameter selection.(2)The value of unmarked multi-graph data is not fully mined.Therefore,a graph neural network combining with graph contrastive learning for semi-supervised multi-graph classification method(GCSS)is proposed.On the one hand,it designs modules that extract feature informa-tion from local and global respectively,and introduces neural networks collaborator(NN collaborator)to complete the collaboration of these two modules,and trains the feature representation of adaptive learning data.On the other hand,graph contrastive learning(GCL)and semi-supervised learning(SSL)are used to make full use of the unmarked from two different learning perspectives,it reduces the model's dependence on labels,etc.A large number of experimental results on the real dataset verify that the prediction performance of the proposed method is better than that of the baseline method.
万方数据
维普
