Discovering Hidden Pattern in Large-scale Dynamically Weighted Directed Network via Latent Factorization of Tensors

Hao Wu, Xin Luo, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 entities. Moreover, as the involved entities increase drastically, it becomes impossible to observe their full interactions at each time span, making a corresponding DWDN high-dimensional and incomplete. However, it contains vital knowledge regarding involved entities' behavior patterns. To extract such knowledge from DWDN, this paper proposes a novel Alternating direction method of multipliers (ADMM)-based Nonnegative Latent-factorization of Tensors (ANLT) model. It adopts two novel ideas: a) building a data density-oriented augmented Lagrangian function for efficiently handling a tensor's incompleteness and nonnegativity; and b) splitting an 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 model convergence. Experimental results on two large-scale DWDNs from a real TIPAS demonstrate that the proposed ANLT model outperforms state-of-the-art models significantly in both computational efficiency and prediction accuracy when addressing missing link prediction on DWDW.

Original languageEnglish (US)
Title of host publication2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PublisherIEEE Computer Society
Pages1533-1538
Number of pages6
ISBN (Electronic)9781665418737
DOIs
StatePublished - Aug 23 2021
Event17th IEEE International Conference on Automation Science and Engineering, CASE 2021 - Lyon, France
Duration: Aug 23 2021Aug 27 2021

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2021-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Country/TerritoryFrance
CityLyon
Period8/23/218/27/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Dynamically Weighted Directed Network
  • Latent Factorization of Tensors
  • Link Prediction
  • Terminal Interaction Pattern Analysis System

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