Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2022
Abstract
Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a posterior estimation in the Bayesian inference framework is used as a score function for TVDBN structure evaluation, and the alternating direction method of multipliers (ADMM) with L-BFGS-B algorithm is used for optimal structure learning. Thorough simulation studies and a real case study are carried out to verify our proposed method’s efficacy and efficiency.
Keywords
Time-varying dynamic Bayesian network, structure learning, segment-wise change, acyclic property, graph Laplacian, ADMM, directed acyclic graph
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces | OS and Networks
Research Areas
Data Science and Engineering
Publication
ACM Transactions on Knowledge Discovery from Data
Volume
16
Issue
6
First Page
1
Last Page
23
ISSN
1556-4681
Identifier
10.1145/3522589
Publisher
Association for Computing Machinery (ACM)
Citation
YANG, Xing; ZHANG, Chen; and ZHENG, Baihua.
Segment-wise time-varying dynamic Bayesian network with graph regularization. (2022). ACM Transactions on Knowledge Discovery from Data. 16, (6), 1-23.
Available at: https://ink.library.smu.edu.sg/sis_research/7264
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://dl.acm.org/doi/10.1145/3522589
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, OS and Networks Commons