Lognormal Infection Times of Online Information Spread

Doerr, Christian; Blenn, Norbert; Mieghem, Piet Van

doi:10.1371/journal.pone.0064349

Cited by 45 publications

(44 citation statements)

References 23 publications

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“…Moreover, with 33 nodes infected it is possible for a domination period to start with precisely the average number of nodes infected, i.e., y s = y. Figure 8 demonstrates that our simulation results are in agreement with the numerical solution to the linear system in (1). For values of y s that occur only rarely the simulated results are further from the numerical results because of a lack of statistics.…”

Section: Domination Time Of Matched Virusessupporting

confidence: 62%

“…Spreading processes on networks are well studied phenomena that can be used to model the spread of diseases, rumors, opinions, and habits in populations or on online social networks, as well as the effect of marketing and product adoption [1][2][3][4]. One of the disease-spreading models that captures, for example, flulike behavior is the susceptibleinfected-susceptible (SIS) model [5].…”

Section: Introductionmentioning

confidence: 99%

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Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

2014

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When two viruses compete for healthy nodes in a simple network and both spreading rates are above the epidemic threshold, only one virus will survive. However, if we prevent the viruses from dying out, rich dynamics emerge. When both viruses are identical, one virus always dominates the other, but the dominating and dominated virus alternate. We show in the complete graph that the domination time depends on the total number of infected nodes at the beginning of the domination period and, moreover, that the distribution of the domination time decays exponentially yet slowly. When the viruses differ moderately in strength and/or speed the weaker and/or slower virus can still dominate the other but for a short time. Interestingly, depending on the number of infected nodes at the start of a domination period, being quicker can be a disadvantage.

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Section: Domination Time Of Matched Virusessupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

2014

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“…This ensures that the state of the process, describing for each node whether it is infected or not, is a Markov chain. We know, however, that these exponential distributions in general do not describe real-life epidemics well [10][11][12]. Here, we extend our results to general waiting times.…”

Section: Introductionmentioning

confidence: 48%

“…When assuming a Weibullean infection time T and an exponential recovery time R, we will show in Sec. IV B1 that the analytic solution of the epidemic threshold equation (2) leads to the epidemic threshold scaling law (11) for large N that was earlier observed in [12] via extensive simulations.…”

Section: Mean-field Approximationmentioning

confidence: 65%

Susceptible-infected-susceptible epidemics on networks with general infection and cure times

2013

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The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous Nintertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

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“…Previous approaches to modeling inter-arrival times of tweets (Perera et al, 2010;Sakaki et al, 2010;Esteban et al, 2012;Doerr et al, 2013) were not complex enough to consider their time varying characteristics. Perera et al inter-arrival times as independent and exponentially distributed with a constant rate parameter.…”

Section: Related Workmentioning

confidence: 99%

Modeling Tweet Arrival Times using Log-Gaussian Cox Processes

Łukasik¹,

Srijith²,

Cohn³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Research on modeling time series text corpora has typically focused on predicting what text will come next, but less well studied is predicting when the next text event will occur. In this paper we address the latter case, framed as modeling continuous inter-arrival times under a logGaussian Cox process, a form of inhomogeneous Poisson process which captures the varying rate at which the tweets arrive over time. In an application to rumour modeling of tweets surrounding the 2014 Ferguson riots, we show how interarrival times between tweets can be accurately predicted, and that incorporating textual features further improves predictions.

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Lognormal Infection Times of Online Information Spread

Cited by 45 publications

References 23 publications

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

Susceptible-infected-susceptible epidemics on networks with general infection and cure times

Modeling Tweet Arrival Times using Log-Gaussian Cox Processes

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