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No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning

Zixing Song · Yifei Zhang · Irwin King

Great Hall & Hall B1+B2 (level 1) #619
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Tue 12 Dec 3:15 p.m. PST — 5:15 p.m. PST


Graph Neural Networks (GNNs) are crucial for machine learning applications with graph-structured data, but their success depends on sufficient labeled data. We present a novel active learning (AL) method for GNNs, extending the Expected Model Change Maximization (EMCM) principle to improve prediction performance on unlabeled data. By presenting a Bayesian interpretation for the node embeddings generated by GNNs under the semi-supervised setting, we efficiently compute the closed-form EMCM acquisition function as the selection criterion for AL without re-training. Our method establishes a direct connection with expected prediction error minimization, offering theoretical guarantees for AL performance. Experiments demonstrate our method's effectiveness compared to existing approaches, in terms of both accuracy and efficiency.

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