Noisy Graph

Noisy graphs, prevalent in real-world data, pose significant challenges for graph neural networks (GNNs) used in tasks like node classification and clustering. Current research focuses on developing robust GNNs and algorithms that mitigate the effects of noisy edges and labels, employing techniques such as graph condensation, meta-weighting, and iterative label propagation and graph purification. These advancements aim to improve the accuracy and reliability of GNNs in various applications by enhancing their resilience to structural and label noise, ultimately leading to more robust and trustworthy graph-based analyses.