BANGS: Game-Theoretic Node Selection for Graph Self-Training

• URL: http://arxiv.org/abs/2410.09348v1
• Date: Sat, 12 Oct 2024 03:31:28 GMT
• Title: BANGS: Game-Theoretic Node Selection for Graph Self-Training
• Authors: Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu,
• Abstract summary: Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model. We propose BANGS, a novel framework that unifies the labeling strategy with conditional mutual information as the objective of node selection. Our approach -- grounded in game theory -- selects nodes in a fashion and provides theoretical guarantees for robustness under noisy objective.
• Score: 39.70859692050266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its prediction performance. While selecting highly confident nodes has proven effective for self-training, this pseudo-labeling strategy ignores the combinatorial dependencies between nodes and suffers from a local view of the distribution. To overcome these issues, we propose BANGS, a novel framework that unifies the labeling strategy with conditional mutual information as the objective of node selection. Our approach -- grounded in game theory -- selects nodes in a combinatorial fashion and provides theoretical guarantees for robustness under noisy objective. More specifically, unlike traditional methods that rank and select nodes independently, BANGS considers nodes as a collective set in the self-training process. Our method demonstrates superior performance and robustness across various datasets, base models, and hyperparameter settings, outperforming existing techniques. The codebase is available on https://github.com/fangxin-wang/BANGS .

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