Communities Detection Algorithm Based on General Stochastic Block Model in Mobile Social Networks

2016 ◽  
Author(s):  
Cong Wan ◽  
Sancheng Peng ◽  
Cong Wang ◽  
Ying Yuan
Keyword(s):  
Social Networks ◽  
Detection Algorithm ◽  
Mobile Social Networks ◽  
Block Model ◽  
Stochastic Block Model ◽  
General Stochastic

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Community Structures ◽  
Block Model ◽  
Real World Data ◽  
Stochastic Block Model ◽  
Dynamic Social Networks ◽  
Influential Nodes

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

scholarly journals Assessing the use of the moving window approaches when monitoring social networks using a degree corrected stochastic block model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abeer A. Zaki ◽  
Nesma A. Saleh ◽  
Mahmoud A. Mahmoud
Keyword(s):  
Social Networks ◽  
Phase I ◽  
Control Charts ◽  
Model Parameters ◽  
Block Model ◽  
Content Type ◽  
Stochastic Block Model ◽  
Ewma Chart ◽  
Window Approach ◽  
Monitoring Phase

PurposeThis study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.Design/methodology/approachA dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.FindingsOur results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.Originality/valueThis study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

Fast Algorithm on Stochastic Block Model for Exploring General Communities

2014 ◽  
Vol 24 (11) ◽  
pp. 2699-2709 ◽  
Author(s):  
Bian-Fang CHAI ◽  
Jian YU ◽  
Cai-Yan JIA ◽  
Jing-Hong WANG
Keyword(s):  
Fast Algorithm ◽  
Block Model ◽  
Stochastic Block Model

Cooperative Contents Distribution in Mobile Social Networks

2014 ◽  
Vol 36 (3) ◽  
pp. 613-625 ◽  
Author(s):  
Hai-Yang HU ◽  
Zhong-Jin LI ◽  
Hua HU ◽  
Ge-Hua ZHAO
Keyword(s):  
Social Networks ◽  
Mobile Social Networks

A Framework for Protecting Privacy on Mobile Social Networks

2021 ◽  
Author(s):  
Seyyed Mohammad Safi ◽  
Ali Movaghar ◽  
Komeil Safikhani Mahmoodzadeh
Keyword(s):  
Social Networks ◽  
Mobile Social Networks
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