Application of Heterogeneous Graph Neural Networks on Attribute Missing Data
Graph data mining is the main task of Heterogeneous Graph Neural Network(HetGNN)model,which uses graph embedding techniques to represent and compute the graph structure data.However,in most practical scenarios,the information provided by heterogeneous graphs is incomplete.Three kinds of graph data mining models including Metapath Aggregated Graph Neural Network(MAGNN),HetGNN and Heterogeneous Graph Attention Network(HAN)model are compared.Experiments are carried out on two publicly available heterogeneous graph datasets.The results show that the performance of MAGNN model is better than that of HAN and HetGNN in the data mining tasks of node classification,which proves its effectiveness.
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