Agent-Based Modeling and Social Networks

2009 ◽  
pp. 63-79 ◽  
Keyword(s):  
Social Networks ◽  
Agent Based Modeling ◽  
Agent Based

Identifying Influencers in Online Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Yifeng Zhang ◽  
Xiaoqing Li ◽  
Te-Wei Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Word Of Mouth ◽  
Agent Based Modeling ◽  
Viral Marketing ◽  
Tie Strength ◽  
Agent Based ◽  
Widespread Acceptance ◽  
Tremendous Amount ◽  
Marketing Messages

Online social networks (OSNs) are quickly becoming a key component of the Internet. With their widespread acceptance among the general public and the tremendous amount time that users spend on them, OSNs provide great potentials for marketing, especially viral marketing, in which marketing messages are spread among consumers via the word-of-mouth process. A critical task in viral marketing is influencer identification, i.e. finding a group of consumers as the initial receivers of a marketing message. Using agent-based modeling, this paper examines the effectiveness of tie strength as a criterion for influencer identification on OSNs. Results show that identifying influencers by the number of strong connections that a user has is superior to doing so by the total number of connections when the strength of strong connections is relatively high compared to that of weak connections or there is a relatively high percentage of strong connections between users. Implications of the results are discussed.

A framework of multilayer social networks for communication behavior with agent-based modeling

SIMULATION ◽  
2013 ◽  
Vol 89 (7) ◽  
pp. 810-828 ◽  
Author(s):  
Yuanzheng Ge ◽  
Liang Liu ◽  
Xiaogang Qiu ◽  
Hongbin Song ◽  
Yong Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Agent Based Modeling ◽  
Communication Behavior ◽  
Agent Based ◽  
Multilayer Social Networks

scholarly journals EQUATION-FREE MULTISCALE COMPUTATIONS IN SOCIAL NETWORKS: FROM AGENT-BASED MODELING TO COARSE-GRAINED STABILITY AND BIFURCATION ANALYSIS

2010 ◽  
Vol 20 (11) ◽  
pp. 3673-3688 ◽  
Author(s):  
A. C. TSOUMANIS ◽  
C. I. SIETTOS ◽  
G. V. BAFAS ◽  
I. G. KEVREKIDIS
Keyword(s):  
Social Networks ◽  
Public Information ◽  
Stationary Solutions ◽  
Agent Based Modeling ◽  
Coarse Grained ◽  
Computational Framework ◽  
Agent Based ◽  
Private And Public ◽  
Multiscale Computations ◽  
Dynamical Information

We focus on the "trijunction" between multiscale computations, bifurcation theory and social networks. In particular, we address how the Equation-Free approach, a recently developed computational framework, can be exploited to systematically extract coarse-grained, emergent dynamical information by bridging detailed, agent-based models of social interactions on networks, with macroscopic, systems-level, continuum numerical analysis tools. For our illustrations, we use a simple dynamic agent-based model describing the propagation of information between individuals interacting under mimesis in a social network with private and public information. We describe the rules governing the evolution of the agents' emotional state dynamics and discover, through simulation, multiple stable stationary states as a function of the network topology. Using the Equation-Free approach we track the dependence of these stationary solutions on network parameters and quantify their stability in the form of coarse-grained bifurcation diagrams.

scholarly journals Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study (Preprint)

2018 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Bojan Simoski ◽  
Eric Fernandes de Mello Araújo ◽  
Kirsten E Bevelander ◽  
William J Burk ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Selection Criteria ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Network Characteristics ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention

BACKGROUND Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. CONCLUSIONS Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

scholarly journals Agent-based Modeling in Tobacco Regulatory Science: Exploring 'What if' in Waterpipe Smoking

2020 ◽  
Vol 6 (3) ◽  
pp. 171-178
Author(s):  
Yong Yang ◽  
Kenneth D. Ward ◽  
Ramzi G. Salloum ◽  
Eric N. Lindblom
Keyword(s):  
Social Networks ◽  
Social Activity ◽  
Agent Based Modeling ◽  
Dynamic Interactions ◽  
Tobacco Products ◽  
Time Activity ◽  
Health Crisis ◽  
Agent Based ◽  
Related Research ◽  
Individual Level

Objectives: Waterpipe tobacco smoking (WTS) is an emerging public health crisis, particularly among youth and young adults. Different from the use of other tobacco products and e-cigarettes, WTS tends to be a social activity occurring among friends or persons associated with social networks. In this paper, we review a potential strategy for WTS-related research. Methods: As a bottom-up computational model, agent-based modeling (ABM) can simulate the actions and interactions of agents, as well as the dynamic interactions between agents and their environments, to gain an understanding of the functioning of a system. ABM is particularly useful for incorporating the influence of social networks in WTS, and capturing people's space-time activity and the spatial distribution of WTS venues. Results: Comprehensive knowledge of WTS-related behaviors at the individual level is needed to take advantage of ABM and use it to examine policies such as the interaction between WTS and cigarette smoking and the effect of flavors used in waterpipe tobacco. Longitudinal and WTS-specific surveys and laboratory experiments are particularly helpful to understand WTS basic mechanisms and elicit individual preferences, respectively. Conclusions: We argue that the uniqueness of WTS makes ABM a promising tool to be used in WTS-related research, as well as understanding use of other tobacco products.

scholarly journals Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns

2021 ◽  
Author(s):  
Juan Miguel Rodriguez-Lopez ◽  
Meike Schickhoff ◽  
Shubhankar Sengupta ◽  
Jürgen Scheffran
Keyword(s):  
Social Networks ◽  
Land Tenure ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Social Structures ◽  
Mobility Patterns ◽  
Agent Based ◽  
Artificial Society ◽  
Linear Behavior ◽  
Non Linear

AbstractThis paper explores the advantages of simulation to raise the question of how digital and social networks affect the mobility in a pastoralist artificial society in the context of environmental degradation. We aim to explore mechanisms and develop scenarios, which are going to be validated through further research. We use a model of a simple pastoralist society in a world without borders to migration by adding the possibility of experiencing the effects of social structures (such as family and friends) and technological networks (e.g., social media). It appears obvious that pastoralist mobility depends on other dimensions as land tenure and traditional knowledge; however, isolating these two effects and experimenting in a simple society allow us to filter the multidimensionality of mobility decisions and concentrate on comparing scenarios in several different social structures and technological network combinations. The results show an expected behavior of more connection and more mobility, and a non-linear emergent behavior where pastoralists wait for a longer amount of time to mobilize when they interact using powerful social and technological networks. This occurs until they decide to move, and then, they mobilize more quickly and strongly than they did when communication was non-existent between them. The literature on migration explains this emergent non-linear behavior.

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