An information spreading model based on online social networks

2018 ◽  
Vol 490 ◽  
pp. 488-496 ◽  
Author(s):  
Tao Wang ◽  
Juanjuan He ◽  
Xiaoxia Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Spreading Model ◽  
Model Based ◽  
Information Spreading

scholarly journals An information spreading model based on relative weight in social network

2015 ◽  
Vol 64 (5) ◽  
pp. 050501
Author(s):  
Wang Jin-Long ◽  
Liu Fang-Ai ◽  
Zhu Zhen-Fang
Keyword(s):  
Social Network ◽  
Relative Weight ◽  
Spreading Model ◽  
Model Based ◽  
Information Spreading

Identification of Influential Online Social Network Users Based on Multi-Features

2016 ◽  
Vol 30 (06) ◽  
pp. 1659015 ◽  
Author(s):  
Qindong Sun ◽  
Nan Wang ◽  
Yadong Zhou ◽  
Zuomin Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Online Social Network ◽  
Real Data ◽  
User Profiles ◽  
Sina Weibo ◽  
Pagerank Algorithm ◽  
Model Based ◽  
User Influence ◽  
Influential Users

The problem of discovering influential users is important to understand and analyze online social networks. The user profiles and interactions between users are significant features to evaluate the user influence. As these features are heterogeneous, it is challengeable to take all of them into a proper model for influence evaluation. In this paper, we propose a model based on personal user features and the adjacent factor to discover influential users in online social networks. Through taking the advantages of Bayesian network and chain principle of PageRank algorithm, the features of the user profiles and interactions are integratedly considered in our model. Based on real data from Sina Weibo data and multiple evaluation metrics of retweet count, tweet count, follower count, etc., the experimental results show that influential users identified by our model are more powerful than the ones identified by single indicator methods and PageRank-based methods.

Mining Information Spreading Based on Users' Retweet Behavior in Twitter

2013 ◽  
Vol 380-384 ◽  
pp. 2866-2870 ◽  
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Wang Qun Lin ◽  
Hu Li
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Power Law Distribution ◽  
Spreading Process ◽  
Spreading Model ◽  
The World ◽  
Information Spread ◽  
Information Spreading

Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.

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