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CSpace
A Novel Approach to Large-Scale Dynamically Weighted Directed Network Representation
Luo, Xin1,2; Wu, Hao1,2; Wang, Zhi3; Wang, Jianjun4; Meng, Deyu5,6
2022-12-01
摘要A dynamically weighted directed network (DWDN) is frequently encountered in various big data-related applications like a terminal interaction pattern analysis system (TIPAS) concerned in this study. It consists of large-scale dynamic interactions among numerous nodes. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant DWDN High Dimensional and Incomplete (HDI). An HDI DWDN, in spite of its incompleteness, contains rich knowledge regarding involved nodes' various behavior patterns. To extract such knowledge from an HDI DWDN, this paper proposes a novel Alternating direction method of multipliers (ADMM)-based Nonnegative Latent-factorization of Tensors (ANLT) model. It adopts three-fold ideas: a) building a data density-oriented augmented Lagrangian function for efficiently handling an HDI tensor's incompleteness and nonnegativity; b) splitting the optimization task in each iteration into an elaborately designed subtask series where each one is solved based on the previously solved ones following the ADMM principle to achieve fast convergence; and c) theoretically proving that its convergence is guaranteed with its efficient learning scheme. Experimental results on six DWDNs from real applications demonstrate that the proposed ANLT outperforms state-of-the-art models significantly in both computational efficiency and prediction accuracy for missing links of an HDI DWDN. Hence, this study proposes a novel and efficient approach to large-scale DWDN representation.
关键词Tensors Computational modeling Numerical models Data models Convergence Analytical models Adaptation models Dynamically weighted directed network terminal interaction pattern analysis system latent factorization of tensors high dimensional and incomplete tensor link prediction representation learning latent feature
DOI10.1109/TPAMI.2021.3132503
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
卷号44期号:12页码:9756-9773
通讯作者Meng, Deyu(dymeng@mail.xjtu.edu.cn)
收录类别SCI
WOS记录号WOS:000880661400086
语种英语