Stochastic Analysis of Rumor Spreading with Multiple Pull Operations

Robin, Frédérique; Séricola, Bruno; Anceaume, Emmanuelle; Mocquard, Yves

doi:10.1007/s11009-021-09911-4

Cited by 4 publications

(8 citation statements)

References 26 publications

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“…Their motivation is to increase the spreading time of the rumor. At any time we suppose that nf ≥ k, otherwise we come back to Robin et al's analysis [18]. Note that in practice, nf is an integer, but this is not necessary for the analysis.…”

Section: The K-pull Protocol In Presence Of Non-cooperative Nodesmentioning

confidence: 99%

“…, n − k, S k,n (i) follows a geometric distribution with parameter P i,i+1 which we denote more simply by p k,n (i). It is shown in [18] that…”

Section: The K-pull Protocol In Absence Of Non-cooperative Nodesmentioning

confidence: 99%

“…The ineffectiveness of the pull protocol stems from the fact that it takes some time before the rumor reaches a phase of exponential growth. In the line of Panagiotou et al's work [16], Robin et al [18] have extended the pull operation with the k-pull operation, which allows an uniformed node to ask for the rumor to a fixed number k − 1 of other nodes in parallel.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Analysis of Rumor Spreading with Multiple Pull Operations in Presence of Non-cooperative Nodes

Kilian,

Anceaume,

Sericola

2023

Lecture Notes in Computer Science

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The recent rise of interest in distributed applications has highlighted the importance of effective information dissemination. The challenge lies in the fact that nodes in a distributed system are not necessarily synchronized, and may fail at any time. This has led to the emergence of randomized rumor spreading protocols, such as push and pull protocols, which have been studied extensively. The k-pull operation, which allows an uninformed node to ask for the rumor from a fixed number of other nodes in parallel, has been proposed to improve the pull algorithm's effectiveness. This paper presents and studies the performance of the k-pull operation in the presence of a certain fraction f of non-cooperative nodes. Our goal is to understand the impact of k on the propagation of the rumor despite the presence of a fraction f of non-collaborative nodes.

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Section: The K-pull Protocol In Presence Of Non-cooperative Nodesmentioning

confidence: 99%

“…, n − k, S k,n (i) follows a geometric distribution with parameter P i,i+1 which we denote more simply by p k,n (i). It is shown in [18] that…”

Section: The K-pull Protocol In Absence Of Non-cooperative Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Analysis of Rumor Spreading with Multiple Pull Operations in Presence of Non-cooperative Nodes

Kilian,

Anceaume,

Sericola

2023

Lecture Notes in Computer Science

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“…As we will see in this section, the results obtained for the discrete-time model of the k-pull rumor spreading, which has been analyzed in [23], cannot be used to deal with the continuous-time model of the k-pull rumor spreading. Indeed, we need here a much more sophisticated analysis.…”

Section: The Modelmentioning

confidence: 99%

Continuous-Time Stochastic Analysis of Rumor Spreading with Multiple Operations

Castella,

Sericola,

Anceaume

et al. 2023

Methodol Comput Appl Probab

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In this paper, we analyze a new asynchronous rumor spreading protocol to deliver a rumor to all the nodes of a large-scale distributed network. This protocol relies on successive pull operations involving k different nodes, with k ≥ 2, and called k-pull operations. Specifically during a k-pull operation, an uninformed node a contacts k − 1 other nodes at random in the network, and if at least one of them knows the rumor, then node a learns it. We perform a detailed study in continuous-time of the total time Θk,n needed for all the n nodes to learn the rumor. These results extend those obtained in a previous paper which dealt with the discrete-time case. We obtain the mean value, the variance and the distribution of Θk,n together with their asymptotic behavior when the number of nodes n tends to infinity.

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“…They replicate the rumor spreading in social networks. In addition to their data processing efficiency (Robin et al, 2021), they have high computational power in aggregated mathematical calculations (Kempe et al, 2003). Recent studies proved that gossip-based machine learning is competitive with federated learning (Hegedűs et al, 2021).…”

Section: Multi-level Decentralized Network For Shmmentioning

confidence: 99%

Leveraging Structural Health Monitoring Data Through Avatars to Extend the Service Life of Mass Timber Buildings

Riggio

Mrissa

Krész

et al. 2022

Front. Built Environ.

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Mass timber construction systems, incorporating engineered wood products as structural elements, are gaining acceptance as a sustainable alternative to multi-story concrete or steel-frame structures. The relative novelty of these systems brings uncertainties on whether these buildings perform long-term as expected. Consequently, several structural health monitoring (SHM) projects have recently emerged to document their behavior. A wide and systematic use of this data by the mass timber industry is currently hindered by limitations of SHM programs. These limitations include scalability, difficulty of data integration, diverse strategies for data collection, scarcity of relevant data, complexity of data analysis, and limited usability of predictive tools. This perspective paper envisions the use of avatars as a Web-based layer on top of sensing devices to support SHM data and protocol interoperability, analysis, and reasoning capability and to improve life cycle management of mass timber buildings. The proposed approach supports robustness, high level and large-scale interoperability and data processing by leveraging the Web protocol stack, overcoming many limitations of conventional centralized SHM systems. The design of avatars is applied in an exemplary scenario of hygrothermal data reconstruction, and use of this data to compare different mold growth prediction models. The proposed approach demonstrates the ability of avatars to efficiently filter and enrich data from heterogeneous sensors, thus overcoming problems due to data gaps or insufficient spatial distribution of sensors. In addition, the designed avatars can provide prediction or reasoning capability about the building, thus acting as a digital twin solution to support building lifecycle management.

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Stochastic Analysis of Rumor Spreading with Multiple Pull Operations

Cited by 4 publications

References 26 publications

Stochastic Analysis of Rumor Spreading with Multiple Pull Operations in Presence of Non-cooperative Nodes

Stochastic Analysis of Rumor Spreading with Multiple Pull Operations in Presence of Non-cooperative Nodes

Continuous-Time Stochastic Analysis of Rumor Spreading with Multiple Operations

Leveraging Structural Health Monitoring Data Through Avatars to Extend the Service Life of Mass Timber Buildings

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