Generalized priority-queue network dynamics: Impact of team and hierarchy

Cho, Won Kuk; Min, Byungjoon; Goh, K.-I.; Kim, I. M.

doi:10.1103/physreve.81.066109

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…The pure priority selection mechanism can also be altered, e.g., to a hybrid priority-FCFS or priority-random selection mechanism. Finally, some kind of coupling between different queues is an important topic, for instance for modeling the interaction between (the tasks of) two people [24][25][26]. An analysis of a useful coupled model can be found in Ref.…”

Section: Discussionmentioning

confidence: 99%

Stochastic queueing-theory approach to human dynamics

et al. 2012

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Recently, numerous studies have shown that human dynamics cannot be described accurately by exponential laws. For instance, Barabási [Nature (London) 435, 207 (2005)] demonstrates that waiting times of tasks to be performed by a human are more suitably modeled by power laws. He presumes that these power laws are caused by a priority selection mechanism among the tasks. Priority models are well-developed in queueing theory (e.g., for telecommunication applications), and this paper demonstrates the (quasi-)immediate applicability of such a stochastic priority model to human dynamics. By calculating generating functions and by studying them in their dominant singularity, we prove that nonexponential tails result naturally. Contrary to popular belief, however, these are not necessarily triggered by the priority selection mechanism.

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Section: Discussionmentioning

confidence: 99%

Stochastic queueing-theory approach to human dynamics

et al. 2012

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“…In these models, prioritization of tasks is considered a major cause of heavy-tailed nature of human dynamics. These models were extended to include interactions between two or more agents inevitable in social system [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Spreading dynamics following bursty human activity patterns

Min

Goh

Vázquez³

2011

Phys. Rev. E

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101

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We study the susceptible-infected model with power-law waiting time distributions P(τ)~τ^{-α}, as a model of spreading dynamics under heterogeneous human activity patterns. We found that the average number of new infections n(t) at time t decays as a power law in the long-time limit, n(t)~t^{-β}, leading to extremely slow prevalence decay. We also found that the exponent in the spreading dynamics β is related to that in the waiting time distribution α in a way depending on the interactions between agents but insensitive to the network topology. These observations are well supported by both the theoretical predictions and the long prevalence decay time in real social spreading phenomena. Our results unify individual activity patterns with macroscopic collective dynamics at the network level.

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“…This also relates to the heavy-tailed temporal distributions observed in human and animal behavior and locomotion [1,2,3,4,5,6,14,15,16,17,18]. A number of approaches have been used to characterize the features of the empirical time statistics of written communication [6,19,20,21,22,23,11,24,25,26,27], with debated indications of scaling behavior for the waiting times, and for their possible modeling through priority queueing. A new method for the analysis of these human reactive phenomena has been recently proposed [13], through which it was shown that, in particular, the mechanisms underpinning the response-time (RT) statistics of written correspondence are best understood, rather than in terms of standard time t, in terms of an agent's activity, i.e.…”

Section: Introductionmentioning

confidence: 99%

New activity pattern in human interactive dynamics

et al. 2015

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We investigate the response function of human agents as demonstrated by written correspondence, uncovering a new universal pattern for how the reactive dynamics of individuals is distributed across the set of each agent's contacts. In long-term empirical data on email, we find that the set of response times considered separately for the messages to each different correspondent of a given writer, generate a family of heavy-tailed distributions, which have largely the same features for all agents, and whose characteristic times grow exponentially with the rank of each correspondent. We furthermore show that this universal behavioral pattern emerges robustly by considering weighted moving averages of the priorityconditioned response-time probabilities generated by a basic prioritization model. Our findings clarify how the range of priorities in the inputs from one's environment underpin and shape the dynamics of agents embedded in a net of reactive relations. These newly revealed activity patterns might be present in other general interactive environments, and constrain future models of communication and interaction networks, affecting their architecture and evolution.

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Generalized priority-queue network dynamics: Impact of team and hierarchy

Cited by 12 publications

References 22 publications

Stochastic queueing-theory approach to human dynamics

Stochastic queueing-theory approach to human dynamics

Spreading dynamics following bursty human activity patterns

New activity pattern in human interactive dynamics

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