Toward Effective Graph Long-Tailed Learning With Noisy Labels.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks

Background:

• Graph long-tailed learning research often overlooks noisy labels in real-world datasets.
• Existing methods individually address graph long-tailed learning or noisy label mitigation, but not both concurrently.
• Noisy labels degrade the generalization and robustness of graph neural networks (GNNs).

Purpose of the Study:

• To propose a general graph learning framework addressing both graph long-tailed learning and noisy labels.
• To enhance the robustness and generalization capabilities of GNNs in challenging data conditions.
• To fill the existing research gap in concurrently tackling these two critical issues.

Main Methods:

• Developed an enhanced robust representation learning technique integrating local and global information.
• Implemented a classifier optimization strategy with calibration and adaptive clean sample selection.
• Designed a framework to specifically alleviate graph long-tailed and noisy label problems.

Main Results:

• The proposed framework demonstrated superior performance compared to strong baseline methods.
• Experiments on real-world datasets validated the effectiveness of the approach.
• The method successfully improved node representation robustness and model generalization.

Conclusions:

• The novel framework effectively addresses the combined challenges of graph long-tailed learning and noisy labels.
• The integrated approach offers significant improvements in GNN performance on complex, real-world graph data.
• This work provides a robust solution for practical applications involving noisy and imbalanced graph datasets.