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Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction | |
Yin, Yanting1; Wu, Yajing2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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ISSN | 1041-4347 |
2024-03-01 | |
卷号 | 36期号:3页码:1311-1327 |
通讯作者 | Yang, Xuebing(yangxuebing2013@ia.ac.cn) ; Yuan, Xiaojie(yuanxj@nankai.edu.cn) |
摘要 | Temporal link prediction on dynamic graphs has attracted considerable attention. Most methods focus on the graph at each timestamp and extract features for prediction. As graphs are directly compressed into feature matrices, the important latent information at each timestamp has not been well revealed. Eventually, the acquisition of dynamic evolution-related patterns is rendered inadequately. In this paper, inspired by the process of Super-Resolution (SR), a novel deep generative model SRG (Super Resolution Graph) is proposed. We innovatively introduce the concepts of the Low-Resolution (LR) graph, which is a single adjacent matrix at a timestamp, and the High-Resolution (HR) graph, which includes the link status of surrounding snapshots. Specifically, two major aspects are considered regarding the construction of the HR graph. For edges, we endeavor to obtain an extensive information transmission description that affects the current link status. For nodes, similar to the SR process, the neighbor relationship among nodes is maintained. In this form, we could predict the link status from a new perspective: Under the supervision of the graph moving average strategy, the conditional normalizing flow effectively realizes the transformation between LR and HR graphs. Extensive experiments on six real-world datasets from different applications demonstrate the effectiveness of our proposal. |
关键词 | Noise measurement Feature extraction Task analysis Superresolution Data mining Predictive models Information processing Temporal link prediction dynamic graphs super-resolution conditional normalizing flow |
DOI | 10.1109/TKDE.2023.3295367 |
关键词[WOS] | NETWORK ; EVOLUTION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R#x0026;D Program of China |
项目资助者 | National Key R#x0026;D Program of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:001167452200004 |
出版者 | IEEE COMPUTER SOC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56955 |
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 | Yang, Xuebing; Yuan, Xiaojie |
作者单位 | 1.Nankai Univ, Coll Comp Sci, Tianjin Key Lab Network & Data Secur Technol, Tianjin 300350, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China |
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 | Yin, Yanting,Wu, Yajing,Yang, Xuebing,et al. Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2024,36(3):1311-1327. |
APA | Yin, Yanting,Wu, Yajing,Yang, Xuebing,Zhang, Wensheng,&Yuan, Xiaojie.(2024).Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,36(3),1311-1327. |
MLA | Yin, Yanting,et al."Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36.3(2024):1311-1327. |
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