Threshold-limited spreading in social networks with multiple initiators

Singh, Pramesh; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, G.

doi:10.1038/srep02330

Cited by 92 publications

(108 citation statements)

References 55 publications

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“…The other is a threshold-based approach (e.g. (Granovetter 1978;Singh et al 2013)), where individuals change their adoption state when a certain threshold of utility is reached. In the case of this study, on the kinship network, the more an individual's peers have adopted the new crop, the more likely that the individual gains information and thus follow suit.…”

Section: Simulation Of Agents' Decision-makingmentioning

confidence: 99%

Multiple peer effects in the diffusion of innovations on social networks: a simulation study

2018

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Peer effects in innovation adoption decisions have been extensively studied. However, the underlying mechanisms of peer effects are generally not explicitly accounted for. Gaps in this knowledge could lead to misestimation of peer effects and inefficient interventions. This study examined the role of two mechanisms-sharing experiences (namely, experience effect) and externalities-in the adoption of an agricultural innovation. By referring to the diffusion process of a new crop in Chinese villages, we developed a simulation model that incorporated experience effect and externality effect on a multiplex network. The model allowed us to estimate the influence of each specific effect and to investigate the interplay of the positive and negative directions of the effects. The main results of simulation experiments were the following: (1) a negative externality effect in the system caused the diffusion of innovation to vary around a middle-level rate, which resulted in a fluctuating diffusion curve rather than a commonly found S-shaped one; (2) in the case of full diffusion, experience effect significantly shaped the diffusion process at the early stage, while externality effect mattered more at the late stage; and (3) network properties (i.e. connectivity, transitivity, and network distance) imposed indirect influence on diffusion through specific peer effects. Overall, our study illustrated the need to understand specific causal mechanisms when studying peer effects. Simulation methods such as agent-based modelling provide an effective approach to facilitate such understanding.

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Section: Simulation Of Agents' Decision-makingmentioning

confidence: 99%

Multiple peer effects in the diffusion of innovations on social networks: a simulation study

2018

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“…Therefore studies have reported that the measurement which is based on artificial models are not suitable in practice [59,60]. Moreover, the spread of diseases and spread of information are found to be different [59,61]. Based on these observations this study is validated using real dynamics of information diffusion in real-world social network similarly to study [18].…”

Section: Evaluation Modelsmentioning

confidence: 97%

“…Unfortunately, these models [58] are developed based on basic belief of human behavior which might not be representative and illustrative of real dynamics information diffusion [18,57]. Therefore studies have reported that the measurement which is based on artificial models are not suitable in practice [59,60]. Moreover, the spread of diseases and spread of information are found to be different [59,61].…”

Section: Evaluation Modelsmentioning

confidence: 99%

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

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Abstract. Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

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“…Perhaps the most influential paper was Watts's study of threshold models as an explanation of cascades or fads [5]. Other papers focused on how the seed nodes' network position influence the subsequent spreading process [11,12]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Social contagion with degree-dependent thresholds

Lee

Holme

2017

Phys. Rev. E

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We investigate opinion spreading by a threshold model in a situation where the influence of people is heterogeneously distributed. We focus on the response of the average opinion as a function between the trend between out-degree (number of neighbors)-effectively the strength of influence of a node-and the threshold for adopting a new product or opinion. We find that if the coupling is very positive, the final state of the system will be a mix of different opinions otherwise it will it converges to a consensus state. We find that this cannot be simply explained as a phase transition, but emerges from a combination of mechanisms and their relative dominance in different regions of parameter space.

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Threshold-limited spreading in social networks with multiple initiators

Cited by 92 publications

References 55 publications

Multiple peer effects in the diffusion of innovations on social networks: a simulation study

Multiple peer effects in the diffusion of innovations on social networks: a simulation study

Identifying the influential spreaders in multilayer interactions of online social networks

Social contagion with degree-dependent thresholds

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