Phase I monitoring of social networks based on Poisson regression profiles

Self Cite

The stochastic block model (SBM) is a random graph model that focuses on partitioning the nodes into blocks or communities. A degree-corrected stochastic block model (DCSBM) considers degree heterogeneity within nodes. Investigation of the type of edge label can be useful for studying networks. We have proposed a labeled degree-corrected stochastic block model (LDCSBM), added the probability of the occurrence of each edge label, and monitored the behavior of this network. The LDCSBM is a dynamic network that varies over time; thus, we applied the monitoring process to both the US Senate voting network and simulated networks by defining structural changes. We used the Shewhart control chart for detecting changes and studied the effect of Phase I parameter estimation on Phase II performance. The efficiency of the model for surveillance has been evaluated using the average run length for estimated parameters.

Section: Introductionmentioning

confidence: 99%

Monitoring a labeled degree‐corrected stochastic block model

Abossedgh

Saghaei

Amiri

2022

Self Cite

“…They employed multivariate EWMA and multivariate CUSUM control charts to monitor the network formation process. Fotuhi et al 20 also modeled counts of communications among people using Poisson regression profiles and utilized extended Hoteling T 2 , F , and a standardized LRT method to detect step changes, drifts, and outliers in the parameters of the fitted Poisson regression profiles. A new modeling and change detection methodology for attributed network streams that exhibit intrinsic dynamic behavior is proposed by Gahrooei and Paynabar 21 .…”

Section: Introductionmentioning

confidence: 99%

A nonparametric change detection approach in social networks

Hazrati‐Marangaloo

Noorossana

2021

A network provides powerful means of representing relationships between entities in complex physical, biological, cyber, and social systems. Any phenomena in those areas may be realized as changes in the structure of the associated networks. Hence, change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection, and health care monitoring. This article proposes a new methodology for monitoring dynamic networks for quick detection of structural changes in network streams and also estimating the location of the change-point. The proposed methodology utilizes the eigenvalues for the adjacency matrices of network snapshots and employs a nonparametric hypothesis to test if the distribution of the eigenvalues for the current snapshot is different from those of the previous ones along a sliding window of reference networks. The statistic of the nonparametric test, energy distance among eigenvalues, is monitored using a one-sided exponentially weighted moving average control chart. Then, after an anomaly detection signal from the monitoring scheme, eigenvalues for the snapshots are employed to calculate the energy statistic at various time steps to locate the change-point.The proposed method is intended to detect two types of structural changes in the networks: (1) change in the communication rates among individuals and (2) change in the community structure of the network. The proposed methodology is applied to both simulated and real-world data. Results indicate that the proposed methodology provides a reliable tool for monitoring networks streams and also estimating change-points locations for precise assessing of the networks under investigation.

“…Using Poisson regression to model the communications between network members, Hoteling T 2 and LRT statistics are developed to monitor the network in phase I in Farahani et al A Poisson regression model for the probability of the number of communications between network members as a function of vertex attributes is proposed in Farahani et al They employed multivariate EWMA and multivariate CUSUM control charts to monitor network formation process. Fotuhi et al also modeled counts of communications among people using Poisson regression profiles. They utilized extended Hoteling T 2 , F , and a standardized LRT method to detect step changes, drift, and outliers in the parameters of the fitted Poisson regression profiles.…”

Section: Introductionmentioning

confidence: 99%

Detecting outbreaks in temporally dependent networks

Hazrati‐Marangaloo

Noorossana

2019

Dynamic networks require effective methods of monitoring and surveillance in order to respond promptly to unusual disturbances. In many applications, it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. In this paper, a dynamic random graph model is proposed that takes into account the past activities of the individuals in the social network and also represents temporal dependency of the network. The model parameters are appearance and disappearance probabilities of an edge which are estimated using a maximum likelihood approach.A generalization of a single path-dependent likelihood ratio test is employed to detect changes in the parameters of the proposed model. Through monitoring the estimated parameters, one can effectively detect structural changes in a temporal-dependent network. The proposed model is employed to describe the behavior of a real network, and its parameters are monitored via dependent likelihood ratio test and multivariate exponentially weighted moving average control chart. Results indicate that the proposed dynamic random graph model is a reliable mean to modeling and detecting changes in temporally dependent networks.KEYWORDS degree-corrected stochastic block model, dynamic network, multivariate exponentially weighted moving average, temporally dependent network longitudinal network data set, the Noordin Top terrorist network from 2001 to 2010, to evaluate the applicability of recently developed methods for social network change detection (SNCD). Application of change detection methods to this historical data set illustrated their potential usefulness including its ability to detect significant changes in the network in response to a series of exogenous factors such as acquisition of bombing materials, capture of key leaders and groups, and death of Noordin himself. Such instances reveal the utmost importance of devising practical network surveillance plans which may help to study complex systems. There are several studies which use process control charts to monitor network behavior. Mcculloh and Carley 6-8 applied cumulative sum (CUSUM) chart to monitor changes in military networks. They employed network metrics, including centrality, betweenness, average link density, and average degree, as features to monitor the network. Since most of the network topological metrics do not follow a normal distribution 9 and also most of the CUSUM procedures are sensitive to the normality assumptions, one should be aware of how normality assumptions may affect the performance of the CUSUM control chart.Considering nine invariants to represent a graph snapshot including network metrics and scaled scan statistics, Park et al 10 proposed a statistic comprised of a weighted sum of the network invariants, and an anomaly was detected when the weighted sum statistic was significantly large. Park et al 11 employed a hypergraph to model a social network and proposed a scan ...