A novel negative feedback information dissemination model based on online social network

2019 ◽  
Vol 513 ◽  
pp. 371-389 ◽  
Author(s):  
Xiaoyang Liu ◽  
Daobing He ◽  
Linfeng Yang ◽  
Chao Liu
Keyword(s):  
Social Network ◽  
Negative Feedback ◽  
Information Dissemination ◽  
Online Social Network ◽  
Feedback Information ◽  
Model Based

Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

2019 ◽  
Vol 63 (11) ◽  
pp. 1689-1703 ◽  
Author(s):  
Xiaoyang Liu ◽  
Daobing He
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
Social Network ◽  
Information Dissemination ◽  
Online Social Network ◽  
Information Propagation ◽  
Propagation Algorithm ◽  
Information Interaction ◽  
Opinion Evolution ◽  
Network Public Opinion

Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

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.

scholarly journals The research of information dissemination model on online social network

2011 ◽  
Vol 60 (5) ◽  
pp. 050501
Author(s):  
Zhang Yan-Chao ◽  
Liu Yun ◽  
Zhang Hai-Feng ◽  
Cheng Hui ◽  
Xiong Fei
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
Online Social Network
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