arXiv:2601.17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However, the presence of label noise in real scenarios poses a significant challenge in learning robust GNNs, and their effectiveness can be severely impacted when dealing with noisy labels on graphs, often stemming from annotation errors or inconsistencies. To address this, in this paper we propose a novel approach called ICGNN that harnesses the structure information of
The proliferation of GNNs in real-world applications has made addressing their robustness to data imperfections like label noise increasingly critical, driving current research in the field.
Improving the robustness of GNNs against noisy data is crucial for reliable AI systems in sensitive applications like bioinformatics and social analysis, directly impacting their real-world utility and adoption.
This research provides a method to make GNNs more dependable and less susceptible to common data quality issues, enhancing trust and performance in graph-structured data learning.
- · AI developers
- · Bioinformatics
- · Social network analysis
- · Data scientists
- · Systems highly vulnerable to label noise
More robust and accurate GNN models in various applications.
Increased adoption of GNNs in critical domains due to improved reliability.
Reduced need for extensive manual data cleaning efforts for GNN-based systems.
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