Knowledge Commons of Institute of Automation,CAS
Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks | |
Yajing, Wu1; Chenyang, Zhang1; Yongqiang, Tang1; Xuebing, Yang1; Yanting, Yin2; Wensheng Zhang1,2,3 | |
发表期刊 | Knowledge-Based Systems |
ISSN | 0950-7051 |
2024-06 | |
卷号 | 294期号:21页码:111714 |
摘要 | eighted or weighted link topologies using historical context. Compared with unweighted links, weighted links can preferably reveal the nature and strength of the interactions among entities. However, weighted links also bring greater challenges because they require subtle structural adjustments and numerical variations to be captured. Existing methods are primarily tailored for unweighted links and most generally suffer from low-quality performance when applied to Weighted Link Prediction (WLP) task. In this study, we propose a novel generative framework that adopts conditional Invertible Neural Networks (INNs) to achieve WLP. The proposed framework leverages the benefits of conditional INNs to exactly optimize the log-likelihood in the latent space conditioned on the historical context, which can be sensitive to minor replacements in real-world systems and derive accurate WLPs. Furthermore, to deal with the long-tail statistical phenomenon of edge weights observed in real life, a tail-adaptive distribution is learned in latent space to capture the tail properties and enhance the model’s ability. To verify the effectiveness of the proposed method, we conduct extensive experiments on four datasets from different systems. The experimental results demonstrate that our model achieves impressive results compared to state-of-the-art competitors. |
WOS记录号 | WOS:001228454600001 |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56697 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Yongqiang, Tang; Xuebing, Yang |
作者单位 | 1.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.Tianjin Key Laboratory of Network and Data Security Technology, College of Computer Science, Nankai University, Tianjin, China 3.Guangzhou University, Guangzhou, Guangdong, China |
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 | Yajing, Wu,Chenyang, Zhang,Yongqiang, Tang,et al. Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks[J]. Knowledge-Based Systems,2024,294(21):111714. |
APA | Yajing, Wu,Chenyang, Zhang,Yongqiang, Tang,Xuebing, Yang,Yanting, Yin,&Wensheng Zhang.(2024).Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks.Knowledge-Based Systems,294(21),111714. |
MLA | Yajing, Wu,et al."Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks".Knowledge-Based Systems 294.21(2024):111714. |
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