Adversarial graph node classification based on unsupervised learning and optimized loss functions

The research field of this paper is unsupervised learning in machine learning, aiming to address the problem of how to simultaneously resist feature attacks and improve model classification performance in unsupervised learning. For this purpose, this paper proposes a method to add an optimized loss function after the graph encoding and representation stage. When the samples are relatively balanced, we choose the cross-entropy loss function for classification. When difficult-to-classify samples appear, an optimized Focal Loss*() function is used to adjust the weights of these samples, to solve the problem of imbalanced positive and negative samples during training. The developed method achieved superior performance accuracy with the values of 0.721 on the Cora dataset, 0.598 on the Citeseer dataset,0.862 on the Polblogs dataset. Moreover, the testing accuracy value achieved by optimized model is 0.745, 0.627, 0.892 on the three benchmark datasets, respectively. Experimental results show that the proposed method effectively improves the robustness of adversarial training models in downstream tasks and reduces potential interference with original data. All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results.