A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data

Fotuhi, Hatef; Amiri, Amirhossein; Taheriyoun, Ali Reza

doi:10.1080/00949655.2019.1657429

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…To the best of the authors' knowledge, there is no research about the real application of polynomial profiles, but some sporadic works could be found in other profile types. For instance, to count a few: monitoring of highway safety [22], calibration system [23] and chemical gas sensors [24] with a general linear model, plasma etch process in semiconductor manufacturing with nonlinear profiles [25], social networks and healthcare with a logistic model [26][27][28], engine procedure in the automotive industry with autocorrelated profiles [17] and customer satisfaction with a nonparametric approach [29]. Furthermore, it is noteworthy to mention that there are several practical applications of control charts for monitoring quality characteristics; see, for example, [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Surveillance of Internet Usage Using a Polynomial Profile Monitoring Scheme

Netshiozwi,

Yeganeh,

Shongwe

et al. 2023

Mathematics

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Control charts, which are one of the major tools in the Statistical Process Control (SPC) domain, are used to monitor a process over time and improve the final quality of a product through variation reduction and defect prevention. As a novel development of control charts, referred to as profile monitoring, the study variable is not defined as a quality characteristic; it is a functional relationship between some explanatory and response variables which are monitored in such a way that the major aim is to check the stability of this model (profile) over time. Most of the previous works in the area of profile monitoring have focused on the development of different theories and assumptions, but very little attention has been paid to the practical application in real-life scenarios in this field of study. To address this knowledge gap, this paper proposes a monitoring framework based on the idea of profile monitoring as a data-driven method to monitor the internet usage of a telecom company. By definition of a polynomial model between the hours of each day and the internet usage within each hour, we propose a framework with three monitoring goals: (i) detection of unnatural patterns, (ii) identifying the impact of policies such as providing discounts and, (iii) investigation of general social behaviour variations in the internet usage. The results shows that shifts of different magnitudes can occur in each goal. With the aim of different charting statistics such as Hoteling T2 and MEWMA, the proposed framework can be properly implemented as a monitoring scheme under different shift magnitudes. The results indicate that the MEWMA scheme can perform well in small shifts and has faster detection ability as compared to the Hoteling T2 scheme.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Surveillance of Internet Usage Using a Polynomial Profile Monitoring Scheme

Netshiozwi,

Yeganeh,

Shongwe

et al. 2023

Mathematics

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“…This estimation is the baseline for the Phase II control charts 12 . Quality control studies elect Phase I 13–16 or Phase II 17–20 in terms of their application and some studies deal with the interaction between these two phases 21–23 …”

Section: Introductionmentioning

confidence: 99%

“…This estimation is the baseline for the Phase II control charts. 12 Quality control studies elect Phase I [13][14][15][16] or Phase II [17][18][19][20] in terms of their application and some studies deal with the interaction between these two phases. [21][22][23] In Section 2, we review the DCSBM and then describe the design of the labeled degree-corrected stochastic block model (LDCSBM) in detail.…”

Section: Introductionmentioning

confidence: 99%

Monitoring a labeled degree‐corrected stochastic block model

Abossedgh

Saghaei

Amiri

2022

Quality & Reliability Eng

Self Cite

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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.

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“…Spectral methods were also reviewed by Komolafe et al [31] and Mazrae Farahani et al [32] used root mean square error (RMSE) to improve the monitoring schemes for anomaly detection in social networks in terms of ARL criterion. Another interesting research by Fotuhi et al [33] proposed a novel approach based on multiple correspondence analysis.…”

mentioning

confidence: 99%

Monitoring Attributed Social Networks Based on Count Data and Random Effects

et al. 2020

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This paper presents a novel approach for the statistical monitoring of online social networks where the edges represent the count of communications between ties at each time stamp. Since the available methods in the literature are limited to the assumption that the set of all interacting individuals is fixed during the monitoring horizon and their corresponding attributes do not change over time, the proposed method tackles these limitations due to the properties of the random effect concepts. Applying appropriate parameters estimation technique involved in a likelihood ratio testing (LRT) approach considering two different statistics, the longitudinal network data are monitored. The performance of the proposed method is verified using numerical examples including simulation studies as well as an illustrative example.

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A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data

Cited by 9 publications

References 43 publications

Data-Driven Surveillance of Internet Usage Using a Polynomial Profile Monitoring Scheme

Data-Driven Surveillance of Internet Usage Using a Polynomial Profile Monitoring Scheme

Monitoring a labeled degree‐corrected stochastic block model

Monitoring Attributed Social Networks Based on Count Data and Random Effects

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