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arXiv · 2306.08210

Uncertainty-Aware Robust Learning on Noisy Graphs

Shuyi Chen·Kaize Ding·Shixiang Zhu
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Abstract

Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in real-world graphs, which can corrupt critical patterns in the data. To address this, we propose a novel uncertainty-aware graph learning framework inspired by distributionally robust optimization. Specifically, we use a graph neural network-based encoder to embed the node features and find the optimal node embeddings by minimizing the worst-case risk through a minimax formulation. Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model that effectively mitigates the impact of uncertainty arising from data noise. Our experimental results demonstrate superior predictive performance over baselines across noisy scenarios.

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Shuyi Chen, Kaize Ding, Shixiang Zhu. 2023-06-14. Uncertainty-Aware Robust Learning on Noisy Graphs. https://doi.org/10.1109/icassp49660.2025.10888672

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