Abstract
Dynamic network data are often encountered in social, biological, and engineering domains. There are two types of variability in dynamic network data: variability of natural evolution and variability due to assignable causes. The latter is the “change” referred to in this article. Accurate and timely change detection from dynamic network data is important. However, it has been infrequently studied, with most of the existing research having focused on community detection, prediction, and visualization. Change detection is a classic research area in Statistical Process Control (SPC), and various approaches have been developed for dynamic data in the form of univariate or multivariate time series but not in the form of networks. We propose a Network State Space Model (NSSM) to characterize the natural evolution of dynamic networks. For tractable parameter estimation of the NSSM, we develop an Expectation Propagation algorithm to produce an approximation for the observation equation of the NSSM and then use Expectation-Maximization integrated with Bayesian Optimal Smoothing to estimate the parameters. For change detection, we further propose a Singular Value Decomposition (SVD)-based method that integrates the NSSM with SPC. A realworld application on Enron dynamic email networks is presented, in which our method successfully detects two known changes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 45-57 |
| Number of pages | 13 |
| Journal | IISE Transactions |
| Volume | 49 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2017 |
Fingerprint
Keywords
- Bayesian optimal smoothing
- Change detection
- Dynamic network modeling
- Expectation propagation
- Statistical process control
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
Modeling and change detection of dynamic network data by a network state space model. / Zou, Na; Li, Jing.
In: IISE Transactions, Vol. 49, No. 1, 2017, p. 45-57.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modeling and change detection of dynamic network data by a network state space model
AU - Zou, Na
AU - Li, Jing
PY - 2017
Y1 - 2017
N2 - Dynamic network data are often encountered in social, biological, and engineering domains. There are two types of variability in dynamic network data: variability of natural evolution and variability due to assignable causes. The latter is the “change” referred to in this article. Accurate and timely change detection from dynamic network data is important. However, it has been infrequently studied, with most of the existing research having focused on community detection, prediction, and visualization. Change detection is a classic research area in Statistical Process Control (SPC), and various approaches have been developed for dynamic data in the form of univariate or multivariate time series but not in the form of networks. We propose a Network State Space Model (NSSM) to characterize the natural evolution of dynamic networks. For tractable parameter estimation of the NSSM, we develop an Expectation Propagation algorithm to produce an approximation for the observation equation of the NSSM and then use Expectation-Maximization integrated with Bayesian Optimal Smoothing to estimate the parameters. For change detection, we further propose a Singular Value Decomposition (SVD)-based method that integrates the NSSM with SPC. A realworld application on Enron dynamic email networks is presented, in which our method successfully detects two known changes.
AB - Dynamic network data are often encountered in social, biological, and engineering domains. There are two types of variability in dynamic network data: variability of natural evolution and variability due to assignable causes. The latter is the “change” referred to in this article. Accurate and timely change detection from dynamic network data is important. However, it has been infrequently studied, with most of the existing research having focused on community detection, prediction, and visualization. Change detection is a classic research area in Statistical Process Control (SPC), and various approaches have been developed for dynamic data in the form of univariate or multivariate time series but not in the form of networks. We propose a Network State Space Model (NSSM) to characterize the natural evolution of dynamic networks. For tractable parameter estimation of the NSSM, we develop an Expectation Propagation algorithm to produce an approximation for the observation equation of the NSSM and then use Expectation-Maximization integrated with Bayesian Optimal Smoothing to estimate the parameters. For change detection, we further propose a Singular Value Decomposition (SVD)-based method that integrates the NSSM with SPC. A realworld application on Enron dynamic email networks is presented, in which our method successfully detects two known changes.
KW - Bayesian optimal smoothing
KW - Change detection
KW - Dynamic network modeling
KW - Expectation propagation
KW - Statistical process control
UR - http://www.scopus.com/inward/record.url?scp=85018441290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018441290&partnerID=8YFLogxK
U2 - 10.1080/0740817X.2016.1198065
DO - 10.1080/0740817X.2016.1198065
M3 - Article
AN - SCOPUS:85018441290
VL - 49
SP - 45
EP - 57
JO - IISE Transactions
JF - IISE Transactions
SN - 2472-5854
IS - 1
ER -