A tensor-based framework for studying eigenvector multicentrality in multilayer networks

Wu, Ming-Chuan; He, Shibo; Zhang, Yongtao; Chen, Jiming; Sun, Youxian; Liu, Yang Yu; Zhang, Junshan; Poor, H. Vincent

doi:10.1073/pnas.1801378116

Cited by 70 publications

(20 citation statements)

References 46 publications

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“…1e), and assist the decision-making of control measures. This is different from previous studies which mainly focused on integrating mathematical models and available statistical data of confirmed cases to characterize the transmission of epidemic diseases [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] , opening up a new perspective to understand the spread of COVID-19.…”

Section: Introductionmentioning

confidence: 74%

Temporal Contact Graph Reveals the Evolving Epidemic Situation of COVID-19

Wu¹,

Li²,

Shen³

et al. 2020

Preprint

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Contact tracing APPs have been recently advocated by many countries (e.g., the United Kingdom, Australia, etc.) as part of control measures on COVID-19. Controversies have been raised about their effectiveness in practice as it still remains unclear how they can be fully utilized to fuel the fight against COVID-19. In this article, we show that an abundance of information can be extracted from contact tracing for COVID-19 prevention and control, providing the first data-driven evidence that supports the wide implementation of such APPs. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location related data contributed by 10,527,737 smartphone users in Wuhan, China. Five time-varying indicators we introduce can accurately capture actual contact trends at individual and population levels, demonstrating that travel restriction in Wuhan played an important role in containing COVID-19. We reveal a strong correlation (Pearson coefficient 0.929) between daily confirmed cases and daily total contacts, which can be utilized as a new and efficient way to evaluate and predict the evolving epidemic situation of COVID-19. Further, we find that there is a prominent distinction of contact behaviors between the infected and uninfected contacted individuals, and design an infection risk evaluation framework to identify infected ones. This can help narrow down the search of high risk contacted individuals for quarantine. Our results indicate that user involvement has an explicit impact on individual-level contact trend estimation while minor impact on situation evaluation, offering guidelines for governments to implement contact tracing APPs.

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Section: Introductionmentioning

confidence: 74%

Temporal Contact Graph Reveals the Evolving Epidemic Situation of COVID-19

Wu¹,

Li²,

Shen³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…stronger scalability, less energy depletion, and stronger robustness [75], [76]. It is of interest to research the structure and function of multilayer networks [77], [78].…”

Section: Performance Analysis Of Aco-based Transmission Protocolsmentioning

confidence: 99%

Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Chen

Wang

et al. 2020

IEEE Access

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Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACObased transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement. INDEX TERMS Wireless sensor networks, transmission protocol, ant colony optimization.

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“…(2) Treat the layers independently and evaluate the centrality in multiple ways [18]. (3) The general centrality measure for multiple networks is calculated directly considering the interactions among layers [15]- [19], [33]- [35].…”

Section: B Centrality Measures For Multiplex Networkmentioning

confidence: 99%

Identifying Multiple Influential Users Based on the Overlapping Influence in Multiplex Networks

Chen

Deng

et al. 2019

IEEE Access

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Online social networks (OSNs) are interaction platforms that can promote knowledge spreading, rumor propagation, and virus diffusion. Identifying influential users in OSNs is of great significance for accelerating the information propagation especially when information is able to travel across multiple channels. However, most previous studies are limited to a single network or select multiple influential users based on the centrality ranking result of each user, not addressing the overlapping influence (OI) among users. In practice, the collective influence of multiple users is not equal to the total sum of these users' influences. In this paper, we propose a novel OI-based method for identifying multiple influential users in multiplex social networks. We first define the effective spreading shortest path (ESSP) by utilizing the concept of spreading rate in order to denote the relative location of users. Then, the collective influence is quantified by taking the topological factor and the location distribution of users into account. The identified users based on our proposed method are central and relatively scattered with a low overlapping influence. With the Susceptible-Infected-Recovered (SIR) model, we estimate our proposed method with other benchmark algorithms. Experimental results in both synthetic and real-world networks verify that our proposed method has a better performance in terms of the spreading efficiency.INDEX TERMS Multiplex networks, influential users, overlapping influence, shortest path.

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A tensor-based framework for studying eigenvector multicentrality in multilayer networks

Cited by 70 publications

References 46 publications

Temporal Contact Graph Reveals the Evolving Epidemic Situation of COVID-19

Temporal Contact Graph Reveals the Evolving Epidemic Situation of COVID-19

Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Identifying Multiple Influential Users Based on the Overlapping Influence in Multiplex Networks

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