Automating the Mean-Field Method for Large Dynamic Gossip Networks

Bakhshi, Rena; Endrullis, Jörg; Endrullis, Stefan; Fokkink, Wan; Haverkort, Boudewijn R.

doi:10.1109/qest.2010.38

Cited by 19 publications

(16 citation statements)

References 25 publications

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“…In [17] the authors apply mean-field models with locations (or classes more generally), which are close to the mean-field models developed in this paper, to the performance evaluation of network systems. PALOMA high-level language to define models of this kind.…”

Section: Related Workmentioning

confidence: 98%

PALOMA: A Process Algebra for Located Markovian Agents

Feng

Hillston

2014

Lecture Notes in Computer Science

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Abstract. We present a novel stochastic process algebra that allows the expression of models representing systems comprised of populations of agents distributed over space, where the relative positions of agents influence their interaction. This language, PALOMA, is given both discrete and continuous semantics and it captures multi-class, multi-message Markovian agent models (M 2 MAM). Here we present the definition of the language and both forms of semantics, and demonstrate the use of the language to model a flu epidemic under various quarantine regimes.

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Section: Related Workmentioning

confidence: 98%

PALOMA: A Process Algebra for Located Markovian Agents

Feng

Hillston

2014

Lecture Notes in Computer Science

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“…[2,23]). The set of 3 The term 'mean field' has its origin in statistical physics and is sometimes used with slightly different meaning in the literature. Here we intend the meaning as defined in [25].…”

Section: Related Workmentioning

confidence: 99%

On-the-fly Fast Mean-Field Model-Checking

Latella¹,

Loreti²,

Massink³

2014

Trustworthy Global Computing

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A novel, scalable, on-the-fly model-checking procedure is presented to verify bounded PCTL properties of selected individuals in the context of very large systems of independent interacting objects. The proposed procedure combines on-the-fly model checking techniques with deterministic mean-field approximation in discrete time. The asymptotic correctness of the procedure is shown and some results of the application of a prototype implementation of the FlyFast model-checker are presented.

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“…This approach is used for an optimized analysis of large-scale systems, allowing for a prediction of the average behavior of the system. The mean-field theory has been applied previously, e.g., to large-scale gossip systems in [23][24][25]. Concisely, the state of the system is represented by a distribution, or a vector of fractions of nodes δ s (t) in each state s at time unit t. The evolution of the stochastic system is governed by a so-called master equation of the form:…”

Section: Modeling Frameworkmentioning

confidence: 99%

Scalable Analysis for Large Social Networks: The Data-Aware Mean-Field Approach

Birkholz

Bakhshi²,

Harige³

et al. 2012

Lecture Notes in Computer Science

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Abstract. Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific model informed from empirical research for predicting links from both network and social properties in large social networks.. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.

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Automating the Mean-Field Method for Large Dynamic Gossip Networks

Cited by 19 publications

References 25 publications

PALOMA: A Process Algebra for Located Markovian Agents

PALOMA: A Process Algebra for Located Markovian Agents

On-the-fly Fast Mean-Field Model-Checking

Scalable Analysis for Large Social Networks: The Data-Aware Mean-Field Approach

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