Learning on Graphs under Label Noise
| العنوان: | Learning on Graphs under Label Noise |
|---|---|
| المؤلفون: | Jingyang Yuan, Xiao Luo, Yifang Qin, Yusheng Zhao, Wei Ju, Ming Zhang |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks, Information Retrieval (cs.IR), Machine Learning (cs.LG), Computer Science - Information Retrieval |
| الوصف: | Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniques often presume that label information of nodes is accurate, which may not be the case in real-world applications. To tackle this issue, we investigate the problem of learning on graphs with label noise and develop a novel approach dubbed Consistent Graph Neural Network (CGNN) to solve it. Specifically, we employ graph contrastive learning as a regularization term, which promotes two views of augmented nodes to have consistent representations. Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise. Moreover, to detect noisy labels on the graph, we present a sample selection technique based on the homophily assumption, which identifies noisy nodes by measuring the consistency between the labels with their neighbors. Finally, we purify these confident noisy labels to permit efficient semantic graph learning. Extensive experiments on three well-known benchmark datasets demonstrate the superiority of our CGNN over competing approaches. Accepted by IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dd6b2a4397aec428fde2700fa96bc23 http://arxiv.org/abs/2306.08194 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5dd6b2a4397aec428fde2700fa96bc23 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |