Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism

2017 ◽  
Vol 5 (6) ◽  
pp. 571-584 ◽  
Author(s):  
Jianhong Chen ◽  
Qinghua Song ◽  
Zhiyong Zhou
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Propagation Model ◽  
Immune Mechanism ◽  
Scale Free Network ◽  
Propagation Process ◽  
Agent Based ◽  
Scale Free ◽  
Rumor Propagation ◽  
Free Network

AbstractTo simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.

Rumor Propagation Model for Complex Network with Non-Uniform Propagation Rates

2014 ◽  
Vol 596 ◽  
pp. 868-872 ◽  
Author(s):  
Rui Sun ◽  
Wan Bo Luo
Keyword(s):  
Mean Field Theory ◽  
Mean Field ◽  
Propagation Rate ◽  
Propagation Model ◽  
Scale Free Network ◽  
Propagation Characteristics ◽  
Affecting Factors ◽  
Scale Free ◽  
Rumor Propagation ◽  
Free Network

Considering propagation characteristics and affecting factors of rumor in real-world complex networks, this paper described different propagation rates of different nodes by introducing the rumor acceptability function. Based on mean-field theory, this paper presented a rumor propagation model with non-uniform propagation rate, and then simulated the behaviour of rumor propagation on scale-free network and calculated the propagation thresholds by corresponding dynamics equation. Theoretical analysis and simulation results show that nodes with different rumor acceptability could lead to slowing the spread of rumors, make positive propagation threshold arise, and effectively contain the outbreak and reduce the risk of rumors.

scholarly journals Hacia la complejidad por la vía de las redes. Nuevas lecciones epistemológicas

2014 ◽  
Author(s):  
Carlos Reynoso
Keyword(s):  
Social Sciences ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scale Free Network ◽  
New Paradigm ◽  
Scale Free ◽  
Network Research ◽  
Free Network

This paper sur veys the reciprocal impacts between Social Network Analysis and the new paradigm of complexity and chaos theories, as well as the emergence of scale-free network research in the twenty-first centur y. This study is embedded in the context of a histor y of the most momentous events in network theor y and practice , from Euler to Barabási, used as a star ting point to interrogate some critical epistemological issues from the viewpoint of contemporar y social sciences.

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.

scholarly journals Effectiveness of Closure of Public Places with Time Delay in Disease Control

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhenggang Wang ◽  
Kwok Yip Szeto ◽  
Frederick Chi-Ching Leung
Keyword(s):  
Social Network ◽  
Time Delay ◽  
Disease Control ◽  
Sir Model ◽  
Scale Free Network ◽  
Public Places ◽  
Scale Free ◽  
The Social ◽  
Free Network ◽  
High Degree

SummaryA theoretical basis for the evaluation of the effciency of quarantine measure is developed in a SIR model with time delay. In this model, the effectiveness of the closure of public places such as schools in disease control, modeled as a high degree node in a social network, is evaluated by considering the effect of the time delay in the identification of the infected. In the context of the SIR model, the relation between the number of infectious individuals who are identified with time delay and then quarantined and those who are not identified and continue spreading the virus are investigated numerically. The social network for the simulation is modeled by a scale free network. Closure measures are applied to those infected nodes with high degrees. The effectiveness of the measure can be controlled by the present value of the critical degree K

Hub Agents Determine Collective Behavior

2015 ◽  
Vol 11 (02) ◽  
pp. 165-181
Author(s):  
Saori Iwanaga ◽  
Akira Namatame
Keyword(s):  
Social Network ◽  
Collective Behavior ◽  
Traffic Congestion ◽  
Scale Free Network ◽  
Collective Phenomena ◽  
Computing Technology ◽  
Scale Free ◽  
Cooperative Relationships ◽  
The Social ◽  
Free Network

There are growing interests for studying collective behavior including the dynamics of markets, the emergence of social norms and conventions and collective phenomena in daily life such as traffic congestion. In our previous work [Iwanaga and Namatame, Collective behavior and diverse social network, International Journal of Advancements in Computing Technology 4(22) (2012) 321–320], we showed that collective behavior in cooperative relationships is affected in the structure of the social network, the initial collective behavior and diversity of payoff parameter. In this paper, we focus on scale-free network and investigate the effect of number of interactions on collective behavior. And we found that choices of hub agents determine collective behavior.

scholarly journals Modeling and Simulating Online Panic in an Epidemic Complexity System: An Agent-Based Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Linjiang Guo ◽  
Yang Li ◽  
Dongfang Sheng
Keyword(s):  
Disease Outbreaks ◽  
Scale Free Network ◽  
Online Forums ◽  
Social Production ◽  
Social Responses ◽  
Agent Based ◽  
Scale Free ◽  
The Social ◽  
Free Network ◽  
The Impact

Following the outbreak of a disease, panic often spreads on online forums, which seriously affects normal economic operations as well as epidemic prevention procedures. Online panic is often manifested earlier than in the real world, leading to an aggravated social response from citizens. This paper conducts sentiment analysis on more than 80,000 comments about COVID-19 obtained from the Chinese Internet and identifies patterns within them. Based on this analysis, we propose an agent-based model consisting of two parts—a revised SEIR model to simulate an offline epidemic and a scale-free network to simulate the Internet community. This model is then used to analyze the effects of the social distancing policy. Assuming the existence of such a policy, online panic is simulated corresponding to different informatization levels. The results indicate that increased social informatization levels lead to substantial online panic during disease outbreaks. To reduce the economic impact of epidemics, we discuss different strategies for releasing information on the epidemic. Our conclusions indicate that announcing the number of daily new cases or the number of asymptomatic people following the peak of symptomatic infections could help to reduce the intensity of online panic and delay the peak of panic. In turn, this can be expected to keep social production more orderly and reduce the impact of social responses on the economy.

scholarly journals A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244107
Author(s):  
Pilar Hernández ◽  
Carlos Pena ◽  
Alberto Ramos ◽  
Juan José Gómez-Cadenas
Keyword(s):  
Numerical Approach ◽  
Time Shift ◽  
Scale Free Network ◽  
Logistic Curve ◽  
Agent Based ◽  
Scale Free ◽  
Free Network ◽  
General Distributions ◽  
New Formulation ◽  
Good Agreement

The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions. We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times. We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists. We find good agreement in both cases. Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations. The formulation allows for a simple numerical approach; software in Julia and Python is provided.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network
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