A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

• Published in: IEEE/CAA journal of automatica sinica Vol. 9; no. 3; pp. 533 - 546
• Main Authors: Wu, Hao, Luo, Xin, Zhou, MengChu, Rawa, Muhyaddin J., Sedraoui, Khaled, Albeshri, Aiiad
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01-03-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); University of Chinese Academy of Sciences,Beijing 100049,China%Department of Electrical and Computer Engineering,New Jersey Institute of Technology,Newark NJ 07102 USA%Center of Research Excellence in Renewable Energy and Power Systems,Department of Electrical and Computer Engineering,Faculty of Engineering,and K.A.CARE Energy Research and Innovation Center,King Abdulaziz University,Jeddah 21589,Saudi Arabia%College of Engineering,King Abdulaziz University,Jeddah 21589,Saudi Arabia%Department of Computer Science,King Abdulaziz University,Jeddah 21481,Saudi Arabia; Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714
• Subjects: Adaptation models; Analytical models; Big data; Computational efficiency; Computational modeling; Convergence; Empirical analysis; Factorization; high dimensional and incomplete (HDI) tensor; latent factorization-of-tensors (LFT); machine learning; Mathematical analysis; Missing data; Network analysis; optimization; Pattern analysis; PI control; Predictive models; Proportional integral derivative; proportional-integral-derivative (PID) controller; Solvers; Tensors; optimization; proportional-integral-derivative (PID) controller; Big data; machine learning; high dimensional and incomplete (HDI) tensor; latent factorization-of-tensors (LFT); missing data
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2021.1004308

A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.