Node classification method based on trusted graph neural network
In order to study the influence of uncertainty of node feature representation on node classification,a node classification method based on trusted graph neural network was proposed.The algorithm used the radial basis function to calculate the distance between nodes,and after obtaining the centroid of various nodes,the classifi-cation label of the nearest centroid was allocated according to the distance to improve the classification performa-nce.Additionally,the distance between unlabeled nodes and centroids is defined as the uncertainty of the model's output.A gradient penalty loss is employed to strengthen the detectability of input variations,loss to strengthen the detectability of input changes,which can effectively detect the distributed outer node samples.The results in classification task are 81.5％,76.2％and 74.6％in terms of AUROC on three public network datasets of Cora,Citeseer and Pubmed,respectively.And the results in the out-of-distribution sample detection task are 83.6％,72.8％and 70.6％in terms of AUROC on three public network datasets of Cora,Citeseer and Pubmed,respectively.It proves that the proposed algorithm can effectively detect the node samples outside the distribution and improve the credibility of node classification,while improving the node classification performance.
万方数据
维普
