ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance

arXiv:2312.08852v2 Announce Type: replace
Abstract: Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such datasets can be cost-prohibitive, leading to the practical use of labels obtained from economically efficient sources such as web searches and user tags. Unfortunately, these labels often come with noise, compromising the generalization performance of deep networks. To tackle this challenge and enhance the robustness of deep learning models against label noise in …

arxiv cs.lg error graphs noise representation representation learning resilient type