Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction
文献类型：期刊论文
| 作者 | Yin, Yanting1; Wu, Yajing2 ; Yang, Xuebing2; Zhang, Wensheng2,3; Yuan, Xiaojie1
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| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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| 出版日期 | 2024-03-01 |
| 卷号 | 36期号:3页码:1311-1327 |
| 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 |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2023.3295367 |
| 通讯作者 | Yang, Xuebing(yangxuebing2013@ia.ac.cn) ; Yuan, Xiaojie(yuanxj@nankai.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NETWORK ; EVOLUTION |
| 资助项目 | National Key R#x0026;D Program of China |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001167452200004 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | National Key R#x0026;D Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56954]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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