Label-informed Graph Structure Learning for Node Classification | |
Wang,Liping1,2; Hu,Fenyu1,2; Wu,Shu1,2,3; Wang,Liang1,2 | |
2021 | |
会议名称 | CIKM 2021 |
会议日期 | November 1–5, 2021 |
会议地点 | Virtual Event, Australia |
摘要 | Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seven node classification benchmark datasets and the results show that our method outperforms or matches the state-of-the-art baselines. |
七大方向——子方向分类 | 数据挖掘 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52181 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Wu,Shu |
作者单位 | 1.Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Artificial Intelligence Research, Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang,Liping,Hu,Fenyu,Wu,Shu,et al. Label-informed Graph Structure Learning for Node Classification[C],2021. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3459637.3482129.pdf(1162KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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