Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction

doi:10.1109/TKDE.2023.3295367

CASIA OpenIR > 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

	Super Resolution Graph With Conditional Normalizing Flows for Temporal Link Prediction
	Yin, Yanting 1; Wu, Yajing2 ; Yang, Xuebing2 ; Zhang, Wensheng2,3 ; Yuan, Xiaojie 1
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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	被引频次：2[WOS] [WOS记录] [WOS相关记录]
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.