Service Adoption Spreading in Online Social Networks

Iñiguez, Gerardo; Ruan, Zhongyuan; Kaski, Kimmo; Kertész, János; Karsai, Márton

doi:10.1007/978-3-319-77332-2_9

Cited by 8 publications

(4 citation statements)

References 82 publications

(157 reference statements)

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“…This is because the standard SIS model does not capture the basic dynamics of social influence and reinforcement, nor the non-linear nature of technological learning/adoption processes 21,22 . This has been confirmed by relatively recent investigations on adoption patterns in online social networks [23][24][25][26][27] . Therefore, complex contagion 28,29 has been proposed as an alternative description in which, for example, threshold mechanisms are introduced in order to account for the effects of peer pressure and social reinforcement mechanisms 30 .…”

Section: Introductionsupporting

confidence: 67%

Multilayer modeling of adoption dynamics in energy demand management

Iacopini

Schäfer

Arcaute

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Due to the emerging of new technologies, the whole electricity system is undergoing transformations on a scale and pace never observed before. In particular, the decentralisation of energy resources and the smart grid have changed the rules of the game and have forced utility services to rethink their relationships with customers. The so-called demand response (DR) seeks to adjust the demand for power instead of adjusting the supply. However, DR business models rely on customer participation and might only be effective if large numbers of customers in close geographic vicinity, e.g. connected to the same transformer, opt in. Here, we introduce a model for the dynamics of service adoption, in which the behaviour of a customer is influenced by its social contacts, in addition of also depending on the specific spatial configuration of other customers in close proximity within the power grid service area. In particular, we use a multiplex network with two layers coupled together, the social layer among customers and the power-grid layer connecting the households. While the adoption process, modelled as an epidemic spreading, runs on the social layer, the node-and timedependent recovery rate of the nodes depends on the states of their neighbours on the power-grid layer, so that the dynamics tends to preserve clusters of infected individuals by making an infected node surrounded by nodes in the same state less keen to recover. Numerical simulations of the model on synthetic and real-world networks show that strong local influence of the costumers actions leads to a discontinuous transition where either no or all nodes in the network are infected, depending on the infection rate and the social pressure to adopt. We find that clusters of local early adopters act as points of high local pressure, helping maintaining adopters, and facilitating an eventual adoption of all nodes. This suggests direct marketing strategies on how to efficiently establish and maintain new technologies such as DR schemes.The electricity system is in the midst of large transformations, and new business models have emerged quickly to facilitate new modes of operation of the electricity supply. The so-called demand response seeks to coordinate demand from a large number of users, through incentives which are usually economic such as variable pricing tariffs. Here, we propose a simple mathematical framework to model consumer behaviours under demand response. Our model considers at the same time social influence and customer benefits to opt into and stay within new control schemes. In our model information about the existence of a contract propagates through the links of a social network, while the geographic proximity of clusters of adopters influences the likelihood of participation by decreasing the likelihood of opting out. The results of our work can help to take informed decision in energy demand management.

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Section: Introductionsupporting

confidence: 67%

Multilayer modeling of adoption dynamics in energy demand management

Iacopini

Schäfer

Arcaute

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

“…Our observations here have important implications for the study of models with kinetic constraints. Threshold models are widely employed models of complex contagion in the social sciences [29,10,50,35], and the AME framework that we have introduced here lends itself to the analysis of the traditional binary-state threshold models [70] as well of more complex multistate threshold models that are the focus of much current attention [49,40].…”

Section: Kinetically Constrained Dynamics and The Necessity Of High Amentioning

confidence: 99%

Multistate Dynamical Processes on Networks: Analysis through Degree-Based Approximation Frameworks

Fennell¹,

Gleeson²

2019

SIAM Rev.

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Multistate dynamical processes on networks, where nodes can occupy one of a multitude of discrete states, are gaining widespread use because of their ability to recreate realistic, complex behaviour that cannot be adequately captured by simpler binary-state models. In epidemiology, multistate models are employed to predict the evolution of real epidemics, while multistate models are used in the social sciences to study diverse opinions and complex phenomena such as segregation. In this paper, we introduce generalized approximation frameworks for the study and analysis of multistate dynamical processes on networks. These frameworks are degree-based, allowing for the analysis of the effect of network connectivity structures on dynamical processes. We illustrate the utility of our approach with the analysis of two specific dynamical processes from the epidemiological and physical sciences. The approximation frameworks that we develop, along with open-source numerical solvers, provide a unifying framework and a valuable suite of tools for the interdisciplinary study of multistate dynamical processes on networks.

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“…We indeed take into consideration both the existence of environmental and personal factors of influence on an individual's behavior. Several studies in information diffusion and influence maximization have reported evidences that, apart from influence coming from social contacts, an individual may be affected by some external event(s) and/or personal reasons to adopt an information (Goyal et al 2010) as well as to delay the adoption of an information (Iniguez et al 2018). In our setting, we tend to reject as true in general, the principle "I agree with my friends' idea and disagree with my foes' idea" (which is also close to the adage "the enemy of my enemy is my friend"), since this would imply that the behavior of a user should be completely determined by the stimuli coming from her/his neighbors.…”

Section: Both Variants ϑ(• •) Range Within the Intervalmentioning

confidence: 99%

Complex influence propagation based on trust-aware dynamic linear threshold models

Caliò

Tagarelli

2019

Appl Netw Sci

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To properly capture the complexity of influence propagation phenomena in real-world contexts, such as those related to viral marketing and misinformation spread, information diffusion models should fulfill a number of requirements. These include accounting for several dynamic aspects in the propagation (e.g., latency, time horizon), dealing with multiple cascades of information that might occur competitively, accounting for the contingencies that lead a user to change her/his adoption of one or alternative information items, and leveraging trust/distrust in the users' relationships and its effect of influence on the users' decisions. To the best of our knowledge, no diffusion model unifying all of the above requirements has been developed so far. In this work, we address such a challenge and propose a novel class of diffusion models, inspired by the classic linear threshold model, which are designed to deal with trust-aware, non-competitive as well as competitive time-varying propagation scenarios. Our theoretical inspection of the proposed models unveils important findings on the relations with existing linear threshold models for which properties are known about whether monotonicity and submodularity hold for the corresponding activation function. We also propose strategies for the selection of the initial spreaders of the propagation process, for both non-competitive and competitive influence propagation tasks, whose goal is to mimic contexts of misinformation spread. Our extensive experimental evaluation, which was conducted on publicly available networks and included comparison with competing methods, provides evidence on the meaningfulness and uniqueness of our models.

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Service Adoption Spreading in Online Social Networks

Cited by 8 publications

References 82 publications

Multilayer modeling of adoption dynamics in energy demand management

Multilayer modeling of adoption dynamics in energy demand management

Multistate Dynamical Processes on Networks: Analysis through Degree-Based Approximation Frameworks

Complex influence propagation based on trust-aware dynamic linear threshold models

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