A two-stage broadcast message propagation model in social networks

2016 ◽  
Vol 462 ◽  
pp. 1286-1293 ◽  
Author(s):  
Dan Wang ◽  
Shun-Jun Cheng
Keyword(s):  
Social Networks ◽  
Propagation Model ◽  
Two Stage ◽  
Broadcast Message ◽  
Message 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.

Modeling rumor propagation and refutation with time effect in online social networks

2018 ◽  
Vol 29 (08) ◽  
pp. 1850068 ◽  
Author(s):  
Yaming Zhang ◽  
Yanyuan Su ◽  
Weigang Li ◽  
Haiou Liu
Keyword(s):  
Social Networks ◽  
Heterogeneous Networks ◽  
Online Social Networks ◽  
Negative Impact ◽  
Time Effect ◽  
Field Equations ◽  
Two Stage ◽  
Dynamical Mechanism ◽  
Rumor Propagation ◽  
Mean Field Equations

Rumor propagation and refutation form an important issue for spreading dynamics in online social networks (OSNs). In this paper, we introduce a novel two-stage rumor propagation and refutation model with time effect for OSNs. The dynamical mechanism of rumor propagation and refutation with time effect is investigated deeply. Then a two-stage model and the corresponding mean-field equations in both homogeneous and heterogeneous networks are obtained. Monte Carlo simulations are conducted to characterize the dynamics of rumor propagation and refutation in both Watts–Strogatz network and Barabási–Albert network. The results show that heterogeneous networks yield the most effective rumor and anti-rumor spreading. Besides, the sooner authority releases anti-rumor and the more attractive anti-rumor is, the less rumor influence is. What’s more, these findings suggest that individuals’ ability to control themselves and identify rumor accurately should be improved to reduce negative impact of rumor effectively. The results are helpful to understand better the mechanism of rumor propagation and refutation in OSNs.

scholarly journals How social networks facilitate collective responses to organizational paradoxes

2019 ◽  
Vol 73 (3) ◽  
pp. 401-428 ◽  
Author(s):  
Joshua Keller ◽  
Sze-Sze Wong ◽  
Shyhnan Liou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Mixed Method ◽  
Organizational Response ◽  
Mixed Method Study ◽  
Two Stage ◽  
Collective Response ◽  
Organizational Paradoxes ◽  
Research Institute

When organizations face paradoxical tensions, such as when they must simultaneously meet scientific and commercial objectives, individuals within the organization also experience tensions. How individuals’ responses to these tensions inform the collective organizational response remains a theoretical and empirical challenge. We address this challenge by introducing a social network perspective. In a two-stage mixed-method study of a research institute in Taiwan, we examined how individuals’ social networks facilitated the organization’s response to a science-commerce paradox. Our results demonstrated that the level of heterogeneity in each individual’s social network influenced how each individual contributed to the organization’s collective response. Specifically, individuals with heterogeneous instrumental networks were more likely to contribute to the organization-wide consensus response, whereas individuals with homogeneous expressive networks were more likely to contribute to a polarized subgroup response. Our findings suggest that individuals’ roles in shaping a collective organizational response to paradoxes depends on who they seek advice from and who they befriend.

Design Improvements for Message Propagation in Malleable Social Networks

2016 ◽  
Vol 25 (6) ◽  
pp. 993-1005 ◽  
Author(s):  
Ram Gopal ◽  
Hooman Hidaji ◽  
Raymond A. Patterson ◽  
Erik Rolland ◽  
Dmitry Zhdanov
Keyword(s):  
Social Networks ◽  
Message Propagation

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.

scholarly journals Key Node Discovery Algorithm Based on Multiple Relationships and Multiple Features in Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xianyong Li ◽  
Ying Tang ◽  
Yajun Du ◽  
Yanjie Li
Keyword(s):  
Social Networks ◽  
Propagation Model ◽  
Multiple Relationships ◽  
Information Propagation ◽  
Multiple Features ◽  
Relational Networks ◽  
Discovery Algorithms ◽  
Relational Network ◽  
Single Relation ◽  
Key Nodes

The key nodes play important roles in the processes of information propagation and opinion evolution in social networks. Previous work rarely considered multiple relationships and features into key node discovery algorithms at the same time. Based on the relational networks including the forwarding network, replying network, and mentioning network in a social network, this paper first proposes an algorithm of the overlapping user relational network to extract different relational networks with same nodes. Integrated with these relational networks, a multirelationship network is established. Subsequently, a key node discovery (KND) algorithm is presented on the basis of the shortest path, degree centrality, and random walk features in the multirelationship network. The advantages of the proposed KND algorithm are proved by the SIR propagation model and the normalized discounted cumulative gain on the multirelationship networks and single-relation networks. The experiment’s results show that the proposed KND method for finding the key nodes is superior to other baseline methods on different networks.

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