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Found 4 papers, 0 papers with code
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
It aims to augment the training set with pseudo-labeled unlabeled nodes with high confidence so as to re-train a supervised model in a self-training cycle.
Unified Robust Training for Graph NeuralNetworks against Label Noise
Learning with label noise has been primarily studied in the context of image classification, but these techniques cannot be directly applied to graph-structured data, due to two major challenges -- label sparsity and label dependency -- faced by learning on graphs.
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
In this paper, we propose a SEmi-supervised Adversarial active Learning (SEAL) framework on attributed graphs, which fully leverages the representation power of deep neural networks and devises a novel AL query strategy in an adversarial way.
