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Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
[article]
2022
arXiv
pre-print
The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previous studies in Semi-Supervised Learning (SSL) for graph have focused on using network predictions to generate soft pseudo-labels or instructing message propagation, which inevitably contains the incorrect prediction due to the over-confident in the predictions. Our proposed Dual-Channel Consistency based Graph
arXiv:2205.03753v1
fatcat:sycvxs6w2vb5xinjlbjbrjucpi