Social network monitoring using a degree corrected stochastic block model / Abeer Abdelaal Zaki Ali ; Supervised Nesma Ali Mahmoud Saleh , Mahmoud Alsaid Mahmoud
Material type: TextLanguage: English Publication details: Cairo : Abeer Abdelaal Zaki Ali , 2020Description: 89 P . : charts ; 25cmOther title:- متابعة شبكات التواصل الاجتماعى باستخدام نموذج الكتل العشوائى مُصحَح الدرجة [Added title page title]
- Issued also as CD
Item type | Current library | Home library | Call number | Copy number | Status | Date due | Barcode | |
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Thesis | قاعة الرسائل الجامعية - الدور الاول | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.03.01.M.Sc.2020.Ab.S (Browse shelf(Opens below)) | Not for loan | 01010110081281000 | |||
CD - Rom | مخـــزن الرســائل الجـــامعية - البدروم | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.03.01.M.Sc.2020.Ab.S (Browse shelf(Opens below)) | 81281.CD | Not for loan | 01020110081281000 |
Thesis (M.Sc.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics
The current study presents an extension of the category of social network analysis studies using control charts. The analysis is performed on two main aspects related to social network monitoring. First,a change detection performance comparison among some network centrality measures (betweenness, closeness, and degree) via control charts is conducted on weighted and unweighted directed networks. Two descriptive measures were identified; which are the average, and standard deviation for each metric. Simulation results show that, in directed networks, the degree centrality measure in most cases is recommended to be used for both weighted and unweighted networks except for some specific situations; in which the betweenness and/ or closeness measures are highly recommended. Also, it can generally be concluded that taking the average of each centrality measure performs better than taking the standard deviation in detecting anomalies
Issued also as CD
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