BANGS: Game-theoretic Node Selection for Graph Self-Training
Keywords: Graph Semi-supervised Learning, Graph Self-training, Game Theory Application
TL;DR: We propose a game-theoretic node selection framework for graph self-training.
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://anonymous.4open.science/r/BANGS-3EA4.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 7521
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