Social network information propagation model based on individual behavior

2017 ◽  
Vol 14 (7) ◽  
pp. 1-15 ◽  
Author(s):  
Lejun Zhang ◽  
Hongjie Li ◽  
Chunhui Zhao ◽  
Xiaoying Lei
Keyword(s):  
Social Network ◽  
Individual Behavior ◽  
Propagation Model ◽  
Information Propagation ◽  
Network Information ◽  
Model Based

Information propagation based on historical memory

2019 ◽  
Vol 30 (12) ◽  
pp. 2050005 ◽  
Author(s):  
Fuzhong Nian ◽  
Anhui Cong ◽  
Rendong Liu
Keyword(s):  
Network Model ◽  
Critical Path ◽  
Historical Memory ◽  
Propagation Model ◽  
Information Propagation ◽  
Core Network ◽  
Memory Strength ◽  
Network Information ◽  
Long Time ◽  
With Memory

This paper aims at the phenomenon of information selective propagation based on historical memory. A network model with memory strength and edge strength is established. The information propagation model with memory-clustering ability is designed with SIR model. And unsupervised learning is introduced to modify the performance. Based on the new network model, the core network and critical path that play a key role in the information propagation are found through the K-shell decomposition method. The research shows that the memory network contains an inertial channel for information propagation, it makes information propagation smooth. And information is selectively propagated in the new network, information is more inclined to propagate between nodes with powerful memory strength and close connections, in other words, people are more willing to propagate information to old friends who have been in contact for a long time instead of new friends.

Information propagation model based on hybrid social factors of opportunity, trust and motivation

2019 ◽  
Vol 333 ◽  
pp. 169-184 ◽  
Author(s):  
Jihong Wan ◽  
Xiaoliang Chen ◽  
Yajun Du ◽  
Mengmeng Jia
Keyword(s):  
Social Factors ◽  
Propagation Model ◽  
Information Propagation ◽  
Model Based

scholarly journals Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyang Liu ◽  
Chao Liu ◽  
Xiaoping Zeng
Keyword(s):  
Mathematical Modeling ◽  
Social Network ◽  
Dynamic Method ◽  
Online Social Network ◽  
Propagation Model ◽  
Information Propagation ◽  
Public Event ◽  
Network Nodes ◽  
Event Information ◽  
Simulation Results

Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.

scholarly journals Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qian Zhang ◽  
Xianyong Li ◽  
Yajun Du ◽  
Jian Zhu
Keyword(s):  
Social Network ◽  
Equilibrium Point ◽  
Control Strategies ◽  
Propagation Model ◽  
Dynamical Analysis ◽  
Information Control ◽  
Information Propagation ◽  
Real Process ◽  
The Social ◽  
Reinforcement Intensity

Due to the inequality of users’ (nodes’) status and the influence of external forces in the progress of the information propagation in a social network, the infected nodes hold different levels of propagation capacity. For this reason, the infected nodes are classified into two categories: the high influential infected nodes and the ordinary influential infected nodes which separately account for 20% and 80% by Pareto’s principle. By borrowing the SEIR epidemic model, this paper proposes an SE2IR information propagation model. Meanwhile, the global asymptotical stabilities of the spread-free equilibrium point and local spread equilibrium point are proved for this model. This paper also puts forward a series of information control strategies including perceived values of users, social reinforcement intensity, and information timeliness in the social network. Through simulation experiments without or with control strategies on a real company e-mail network dataset, this paper verifies the stability and correctness of the model and the feasibility and effectiveness of the control strategies in the information propagation process, presenting that the model is closer to the real process of the information propagation in the social network.

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