SFTRD: A novel information propagation model in heterogeneous networks: Modeling and restraining strategy

2019 ◽  
Vol 524 ◽  
pp. 475-490
Author(s):  
Chunyan Sang ◽  
Tun Li ◽  
Sirui Tian ◽  
Yunpeng Xiao ◽  
Guangxia Xu
Keyword(s):  
Heterogeneous Networks ◽  
Propagation Model ◽  
Information Propagation

scholarly journals A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 47-70
Author(s):  
Santhoshkumar Srinivasan ◽  
Dhinesh Babu L. D.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Propagation Model ◽  
Information Propagation ◽  
Control Approach ◽  
Rumor Propagation ◽  
Immunization Strategies ◽  
New Information ◽  
The Individual ◽  
Rumor Control

Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.

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.

A two-layer SIR information propagation model with heterogeneity based on coupled network

2019 ◽  
Vol 76 (3) ◽  
pp. 1657-1679 ◽  
Author(s):  
Tongrang Fan ◽  
Wanting Qin ◽  
Wenbin Zhao ◽  
Feng Wu ◽  
Jianmin Wang
Keyword(s):  
Propagation Model ◽  
Information Propagation

Evolution of node impact based on secondary propagation

2019 ◽  
Vol 34 (02) ◽  
pp. 2050027
Author(s):  
Fuzhong Nian ◽  
Kai Gao
Keyword(s):  
Real Life ◽  
Propagation Model ◽  
Information Propagation ◽  
Actual Situation ◽  
Scale Free Network ◽  
Scale Free ◽  
Initial Stage ◽  
Free Network ◽  
Three Stages ◽  
Stable Stage

In real life, the propagation ability of the information disseminator is one of the important factors which is determined to propagate information. The influence of the node, which is altered with time, is proposed to reflect the propagation ability of the information disseminator for the significance of the information propagation in the actual situation in this paper. Therefore, the influence of the node is divided into the high-impact node and the low-impact node. Furthermore, the SSIR information propagation model is proposed and the dynamic BA scale-free network is constructed to carry out evolution of node impact based on secondary propagation experiments. The experiment results indicate three stages, including the initial stage, the rapidly rising stage and the stable stage. The propagation details of the different messages are distinct. However, the trend of propagation is similar.

Modelling multi-topic information propagation in online social networks based on resource competition

2016 ◽  
Vol 43 (3) ◽  
pp. 342-355 ◽  
Author(s):  
Liyuan Sun ◽  
Yadong Zhou ◽  
Xiaohong Guan
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Resource Competition ◽  
Propagation Model ◽  
Small Scale ◽  
Model Parameters ◽  
Information Propagation ◽  
Practical Applications ◽  
The Impact

Understanding information propagation in online social networks is important in many practical applications and is of great interest to many researchers. The challenge with the existing propagation models lies in the requirement of complete network structure, topic-dependent model parameters and topic isolated spread assumption, etc. In this paper, we study the characteristics of multi-topic information propagation based on the data collected from Sina Weibo, one of the most popular microblogging services in China. We find that the daily total amount of user resources is finite and users’ attention transfers from one topic to another. This shows evidence on the competitions between multiple dynamical topics. According to these empirical observations, we develop a competition-based multi-topic information propagation model without social network structure. This model is built based on general mechanisms of resource competitions, i.e. attracting and distracting users’ attention, and considers the interactions of multiple topics. Simulation results show that the model can effectively produce topics with temporal popularity similar to the real data. The impact of model parameters is also analysed. It is found that topic arrival rate reflects the strength of competitions, and topic fitness is significant in modelling the small scale topic propagation.

Precautionary rumor containment via trustworthy people in social networks

2016 ◽  
Vol 08 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Lidan Fan ◽  
Weili Wu ◽  
Kai Xing ◽  
Wonjun Lee
Keyword(s):  
Greedy Algorithm ◽  
Constant Factor ◽  
Propagation Model ◽  
Information Propagation ◽  
Precautionary Measure ◽  
Real World Data ◽  
Rumor Propagation ◽  
Entire Network ◽  
Np Hardness ◽  
The Greedy Algorithm

In a social network, rumor containment is vital, as the diffusion of a rumor will bring terrible results. Precautionary measure can be used to control rumor propagation: Anticipating the spread of a rumor, one can (1) select a set of trustworthy people (TP) in the network, (2) alert the TP about the rumor, and (3) ask the TP to protect their neighbors by sending out alerts. In this paper, we study the problem of how to select the least number of TP, satisfying the requirement that the entire network is protected by the alerts that the TP send. We propose an asymmetric trust (AT) information propagation model. Under this model, we study the Least Number TP Selection (LNTS) problem, establish its NP-hardness and reformulate it as a minimum submodular cover problem. As a result, the Greedy Algorithm is a constant-factor approximation algorithm. Using real-world data, we evaluate the performance of the Greedy Algorithm, and compare it with other algorithms. Experimental results indicate that the Greedy Algorithm performs the best among its competitors.

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