HPOSN: A Novel Online Social Network Model Based on Hybrid P2P

2015 ◽  
Author(s):  
Jiwei Wang ◽  
Fangai Liu ◽  
Xu Li ◽  
Haoran Liu ◽  
Xiaohui Zhao
Keyword(s):  
Social Network ◽  
Network Model ◽  
Online Social Network ◽  
Model Based ◽  
Hybrid P2p

Stability analysis of a fractional online social network model

2020 ◽  
Vol 178 ◽  
pp. 625-645
Author(s):  
John R. Graef ◽  
Lingju Kong ◽  
Andrew Ledoan ◽  
Min Wang
Keyword(s):  
Social Network ◽  
Stability Analysis ◽  
Network Model ◽  
Online Social Network

scholarly journals An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico

2020 ◽  
Vol 7 (2-1) ◽  
pp. 6-18
Author(s):  
Manuel Suárez Gutiérrez ◽  
José Luis Sánchez Cervantes ◽  
Mario Andrés Paredes Valverde
Keyword(s):  
Social Network ◽  
Network Model ◽  
Social Perception ◽  
Online Social Network ◽  
Time Lapse ◽  
High Impact ◽  
Specific Time

This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan

Ensemble method: Community detection based on game theory

2014 ◽  
Vol 28 (30) ◽  
pp. 1450211 ◽  
Author(s):  
Xia Zhang ◽  
Zhengyou Xia ◽  
Shengwu Xu ◽  
J. D. Wang
Keyword(s):  
Game Theory ◽  
Social Network ◽  
Community Detection ◽  
Online Social Network ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Model ◽  
Model Based ◽  
Active Research

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

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