Towards a temporal network analysis of interactive WiFi users

Zhang, Yan; Wang, Lin; Zhang, Yi-Qing; Li, Xiang

doi:10.1209/0295-5075/98/68002

Cited by 60 publications

(36 citation statements)

References 28 publications

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“…The data analysis reveals an unexpected feature that the distribution of the number of distinct persons each individual encounters every day only has a small squared coefficient of variance [162,164,166,167,168,169], irrespective of the specific social contexts (see Fig. 10).…”

Section: Studies Using Digital Equipmentmentioning

confidence: 98%

“…[160,161] referred to the Bluetooth technique embedded in mobile phones, which collects the proxy data of person-to-person interactions of MIT Media Laboratory in the Reality Mining program; with the help of wireless sensors, the social experiment was conducted to trace the close proximity contacts among the members of an American high school [162]; Ref. [163,164,165,166] considered the active Radio Frequency Identification Devices (RFID) to establish a flexible platform recording the face-to-face proximity contacts among volunteers, which had been deployed in various social contexts such as conference, museum, hospital, and primary school; and the WiFi accessing data among all students and staffs were also analyzed as the indirect proxy records of their concurrent communications in one Chinese University [167,168]. Compared with the above-mentioned data of questionnaires, the electronic data generated by digital experiments is more accurate and objective.…”

Section: Studies Using Digital Equipmentmentioning

confidence: 99%

See 1 more Smart Citation

Coupled disease–behavior dynamics on complex networks: A review

Wang

Andrews

et al. 2015

Physics of Life Reviews

Self Cite

451

304

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It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

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Section: Studies Using Digital Equipmentmentioning

confidence: 98%

Section: Studies Using Digital Equipmentmentioning

confidence: 99%

Coupled disease–behavior dynamics on complex networks: A review

Wang

Andrews

et al. 2015

Physics of Life Reviews

Self Cite

451

304

View full text Add to dashboard Cite

show abstract

“…These networks include spatial network (whose nodes occupy a precise position in two or three-dimensional Euclidean space, and whose edges are real physical connections) [214][215][216][217], adaptive networks [218], temporal or time-varying networks [219][220][221] (for more details, please also see Section 11).…”

Section: Type Of Networkmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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“…It has been found that the scale-free networks [22–26] might provide a unified platform to foster the cooperation for nearly all main social dilemmas, such as PDG, snowdrift game (SDG), public goods game (PGG) etc, and the evolutionary outcome is also very robust against the normalization of payoffs. Furthermore, the heterogeneity and diversity [27–35], which is considered by the individual behavior as well as those concerned in terms of players having different degree within a network, has been proved to be a very effective manner to promote the cooperation, and indeed many co-evolutionary rules [36–39] have also been introduced to spontaneously create the heterogeneous state so as to maintain the surprisingly high cooperation level (see [40] for a full review).…”

Section: Introductionmentioning

confidence: 99%

Utility Evaluation Based on One-To-N Mapping in the Prisoner’s Dilemma Game for Interdependent Networks

Wang

Liu

et al. 2016

PLoS ONE

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In the field of evolutionary game theory, network reciprocity has become an important means to promote the level of promotion within the population system. Recently, the interdependency provides a novel perspective to understand the widespread cooperation behavior in many real-world systems. In previous works, interdependency is often built from the direct or indirect connections between two networks through the one-to-one mapping mode. However, under many realistic scenarios, players may need much more information from many neighboring agents so as to make a more rational decision. Thus, beyond the one-to-one mapping mode, we investigate the cooperation behavior on two interdependent lattices, in which the utility evaluation of a focal player on one lattice may not only concern himself, but also integrate the payoff information of several corresponding players on the other lattice. Large quantities of simulations indicate that the cooperation can be substantially promoted when compared to the traditionally spatial lattices. The cluster formation and phase transition are also analyzed in order to explore the role of interdependent utility coupling in the collective cooperation. Current results are beneficial to deeply understand various mechanisms to foster the cooperation exhibited inside natural, social and engineering systems.

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Towards a temporal network analysis of interactive WiFi users

Cited by 60 publications

References 28 publications

Coupled disease–behavior dynamics on complex networks: A review

Coupled disease–behavior dynamics on complex networks: A review

Statistical physics of vaccination

Utility Evaluation Based on One-To-N Mapping in the Prisoner’s Dilemma Game for Interdependent Networks

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