Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm

St-Onge, Guillaume; Young, Jean-Gabriel; Dubé, Louis J.

doi:10.1016/j.cpc.2019.02.008

Cited by 30 publications

(27 citation statements)

References 54 publications

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“…For our simulations, we made use of the exceptionally fast implementation by St-Onge et al [76]. For simplicity, we set time units to be equal to the expected recovery time (such that γ = 1) and vary β over a large enough range to explore outbreaks of varied sizes.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Immunization strategies in networks with missing data

Smith

Gauthier

et al. 2020

PLoS Comput Biol

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Network-based intervention strategies can be effective and cost-efficient approaches to curtailing harmful contagions in myriad settings. As studied, these strategies are often impractical to implement, as they typically assume complete knowledge of the network structure, which is unusual in practice. In this paper, we investigate how different immunization strategies perform under realistic conditions-where the strategies are informed by partiallyobserved network data. Our results suggest that global immunization strategies, like degree immunization, are optimal in most cases; the exception is at very high levels of missing data, where stochastic strategies, like acquaintance immunization, begin to outstrip them in minimizing outbreaks. Stochastic strategies are more robust in some cases due to the different ways in which they can be affected by missing data. In fact, one of our proposed variants of acquaintance immunization leverages a logistically-realistic ongoing survey-intervention process as a form of targeted data-recovery to improve with increasing levels of missing data. These results support the effectiveness of targeted immunization as a general practice. They also highlight the risks of considering networks as idealized mathematical objects: overestimating the accuracy of network data and foregoing the rewards of additional inquiry.

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Section: Plos Computational Biologymentioning

confidence: 99%

Immunization strategies in networks with missing data

Smith

Gauthier

et al. 2020

PLoS Comput Biol

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“…Rejection sampling for the efficient simulation of Markovian stochastic processes on complex networks has been proposed recently [24,25,26,34], but not for the non-Markovian case where arbitrary distributions for the inter-event times are considered.…”

Section: Our Methodsmentioning

confidence: 99%

“…However, both methods require computationally expensive updating of an agent's neighborhood in each simulation step, which renders them inefficient for large-scale networks. In the context of Markovian processes on networks, it has recently been shown that rejection-based simulation can overcome this limitation [24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Rejection-Based Simulation of Non-Markovian Agents on Complex Networks

Großmann

Bortolussi

Wolf

2019

Complex Networks and Their Applications VIII

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Stochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patterns in real-world systems. Stochastic simulations are often the preferred-sometimes the only feasible-way to investigate such systems. Previous research focused primarily on Markovian models where the random time until an interaction happens follows an exponential distribution. In this work, we study a general framework to model systems where each agent is in one of several states. Agents can change their state at random, influenced by their complete neighborhood, while the time to the next event can follow an arbitrary probability distribution. Classically, these simulations are hindered by high computational costs of updating the rates of interconnected agents and sampling the random residence times from arbitrary distributions. We propose a rejection-based, event-driven simulation algorithm to overcome these limitations. Our method over-approximates the instantaneous rates corresponding to inter-event times while rejection events counterbalance these over-approximations. We demonstrate the effectiveness of our approach on models of epidemic and information spreading.Here, c i , c r ∈ R ≥0 denote the infection and recovery rate constants, respectively. Note that the infection rate is proportional to the number of infected neighbors whereas the rate of recovery is independent from neighboring agents. Moreover,

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“…. In this paper, the SIS prevalence is generated by an event-driven simulation based on the Gillespie algorithm (Gillespie 1977;Liu and Van Mieghem 2017;St-Onge et al 2019).…”

Section: The Sis Processmentioning

confidence: 99%

Inferring network properties based on the epidemic prevalence

Liu

Mieghem

2019

Appl Netw Sci

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Dynamical processes running on different networks behave differently, which makes the reconstruction of the underlying network from dynamical observations possible. However, to what level of detail the network properties can be determined from incomplete measurements of the dynamical process is still an open question. In this paper, we focus on the problem of inferring the properties of the underlying network from the dynamics of a susceptible-infected-susceptible epidemic and we assume that only a time series of the epidemic prevalence, i.e., the average fraction of infected nodes, is given. We find that some of the network metrics, namely those that are sensitive to the epidemic prevalence, can be roughly inferred if the network type is known. A simulated annealing link-rewiring algorithm, called SARA, is proposed to obtain an optimized network whose prevalence is close to the benchmark. The output of the algorithm is applied to classify the network types.

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Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm

Cited by 30 publications

References 54 publications

Immunization strategies in networks with missing data

Immunization strategies in networks with missing data

Rejection-Based Simulation of Non-Markovian Agents on Complex Networks

Inferring network properties based on the epidemic prevalence

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