A Hybrid Hierarchical Graph Classification Model Based on BiLSTM
Graph classification is a very important and challenging problem in many fields such as chemistry and bioinformatics,and the GNN model is the mainstream method for graph classification problems.The existing GNN models use convolution operations to aggregate the information of neighbor nodes,and then generate the coarsening-graphs by pooling method.However,the bidirectional dependencies of the readout graph after each convolution cannot be captured by the pooling method alone.In order to extract more sufficient feature information,a hybrid hierarchical model is proposed in this paper,which first extracts node feature information and structural feature information respec-tively,then fuses the feature information,and then uses BiLSTM to capture the bidirectional dependencies between different levels of readout graphs,so as to extract richer feature information.The experimental results show that com-pared with the comparison model,the accuracy of the model has been significantly improved
NETL
NSTL
万方数据
