Uncertainty-Aware Robust Learning on Noisy Graphs

• URL: http://arxiv.org/abs/2306.08210v2
• Date: Thu, 13 Mar 2025 14:30:06 GMT
• Title: Uncertainty-Aware Robust Learning on Noisy Graphs
• Authors: Shuyi Chen, Kaize Ding, Shixiang Zhu,
• Abstract summary: We propose a novel uncertainty-aware graph learning framework inspired by distributionally robust optimization.<n>We use a graph neural network-based encoder to embed the node features and find the optimal node embeddings.<n>Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model.
• Score: 22.848589361600382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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