Containment of rumor spread in complex social networks

Lan, Yang; Li, Zhiwu; Giua, Alessandro

doi:10.1016/j.ins.2019.07.055

Cited by 135 publications

(40 citation statements)

References 47 publications

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“…It is crucial to come up with a means to convert the ICTPN model into ladder diagrams (LDs) for implementation on programmable logic controllers (PLCs) [24], using adaptive control techniques [39], or using surveillance systems [40]. We will also consider to model and schedule automated manufacturing systems [41] and social networks [42] using the proposed model methodology. Funding: This research was funded by King Saud University, grant number (RSP-2019/62), and the APC was funded by King Saud University.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems

Kaid

Al-Ahmari

et al. 2020

Processes

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The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current model has to be modified. Based on the resultant ICTPN model, deadlock-free, conservative, and reversible behavioral properties, among others, are guaranteed. The developed ICTPN model was tested and validated using the GPenSIM tool and compared with existing methods from the literature.

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Section: Discussionmentioning

confidence: 99%

Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems

Kaid

Al-Ahmari

et al. 2020

Processes

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“…We intend to implement real-time middleware that embeds the proposed contribution and some intelligent optimization methods [40], [42]. In the future, we plan to use the developed method to the social networks [43], [44] and discret event systems [45]- [47].…”

Section: Discussionmentioning

confidence: 99%

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

et al. 2020

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This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods.

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“…However, predicting the infection order is important in many scenarios. For example, it is helpful for blocking rumor spread to know who will be the next infected node [15,16]. Second, the existing methods often assume that information diffusion follows a parametric model such as Independent Cascade (IC) model [17] and Susceptible-Infected (SI) model [18].…”

Section: Introductionmentioning

confidence: 99%

Deep Collaborative Embedding for information cascade prediction

Zhao

Yang

Lin

et al. 2020

Knowledge-Based Systems

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Recently, information cascade prediction has attracted increasing interest from researchers, but it is far from being well solved partly due to the three defects of the existing works. First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion. Second, the existing works often ignore the prediction of the infection order, which also plays an important role in social network analysis. At last, the existing works often depend on the requirement of underlying diffusion networks which are likely unobservable in practice. In this paper, we aim at the prediction of both node infection and infection order without requirement of the knowledge about the underlying diffusion mechanism and the diffusion network, where the challenges are two-fold. The first is what cascading characteristics of nodes should be captured and how to capture them, and the second is that how to model the non-linear features of nodes in information cascades. To address these challenges, we propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction, which can capture not only the node structural property but also two kinds of node cascading characteristics. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration, in which way the non-linearity of information cascades * Ning Yang and Tao Lin share the corresponding authorship.can be effectively captured. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.

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Containment of rumor spread in complex social networks

Cited by 135 publications

References 47 publications

Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems

Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

Deep Collaborative Embedding for information cascade prediction

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