Internet information spreading model research

2008 ◽  
Author(s):  
Yu Cao ◽  
Yong Fang ◽  
Anming Zhou ◽  
Li Chen
Keyword(s):  
Spreading Model ◽  
Model Research ◽  
Internet Information ◽  
Information Spreading

scholarly journals Dynamics of an Information Spreading Model with Isolation

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xia-Xia Zhao ◽  
Jian-Zhong Wang
Keyword(s):  
Mathematical Model ◽  
Hopf Bifurcation ◽  
Limit Cycle ◽  
Real World ◽  
Modern Society ◽  
The Real ◽  
Spreading Model ◽  
Wide Range ◽  
Bistable Phenomenon ◽  
Information Spreading

Information plays an important role in modern society. In this paper, we presented a mathematical model of information spreading with isolation. It was found that such a model has rich dynamics including Hopf bifurcation. The results showed that, for a wide range of parameters, there is a bistable phenomenon in the process of information spreading and thus the information cannot be well controlled. Moreover, the model has a limit cycle which implies that the information exhibits periodic outbreak which is consistent with the observations in the real world.

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

scholarly journals Identifying the Influential Latent Edges for Promoting the Co-SIR Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dan Yang ◽  
Liming Pan ◽  
Zhidan Zhao ◽  
Tao Zhou
Keyword(s):  
Real World ◽  
Message Passing ◽  
Sir Model ◽  
Research Area ◽  
Disease Outbreak ◽  
Limited Attention ◽  
Original Network ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Information Spreading

The network-based cooperative information spreading is a widely existing phenomenon in the real world. For instance, the spreading of disease outbreak news and disease prevention information often coexist and interact with each other on the Internet. Promoting the cooperative spreading of information in network-based systems is a subject of great importance in both theoretical and practical perspectives. However, very limited attention has been paid to this specific research area so far. In this study, we propose an effective approach for identifying the influential latent edges (that is, the edges that do not originally exist) which, if added to the original network, can promote the cooperative susceptible-infected-recovered (co-SIR) dynamics. To be specific, we first obtain the probabilities of each nodes being in different node states by the message-passing approach. Then, based on the state probabilities of nodes obtained, we come up with an indicator, which incorporates both the information of network topology and the co-SIR dynamics, to measure the influence of each latent edge in promoting the co-SIR dynamics. Thus, the most influential latent edges can be located after ranking all the latent edges according to their quantified influence. We verify the rationality and superiority of the proposed indicator in identifying the influential latent edges of both synthetic and real-world networks by extensive numerical simulations. This study provides an effective approach to identify the influential latent edges for promoting the network-based co-SIR information spreading model and offers inspirations for further research on intervening the cooperative spreading dynamics from the perspective of performing network structural perturbations.

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.

scholarly journals Information Spreading on Activity-Driven Temporal Networks with Two-Step Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Linfeng Zhong ◽  
Xiaoyu Xue ◽  
Yu Bai ◽  
Jin Huang ◽  
Qing Cheng ◽  
...  
Keyword(s):  
Network Science ◽  
Critical Mass ◽  
Transmission Probability ◽  
Temporal Network ◽  
Temporal Networks ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Information Spreading ◽  
Initial Seed ◽  
Markovian Approach

Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations.

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