Analyzing the potential impact of BREXIT on the European research collaboration network

Mingueza, F. Bauzá; Ruiz-Manzanares, G.; Pérez-Sienes, Leticia; Tarancón, A.; Íñiguez, D.; Gómez‐Gardeñes, Jesús

doi:10.1063/1.5139019

Cited by 4 publications

(2 citation statements)

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“…Furthermore, two papers explore networks of extreme rainfall events, i.e., using networks as an abstract representation of complex data. 14,17 The paper by Bauzá et al 13 explores the influence of BREXIT (a rare political event of massive socioeconomic consequences) on the European research collaboration network. The authors constructed the collaboration networks of European research projects based on the H2020 programs of Excellent Science (P1), Industrial Leadership (P2), and Societal Challenges (P3) between 2014 and 2018.…”

Section: A Network Science Based Analysismentioning

confidence: 99%

“…Here we include a few papers discussing the theoretical aspects and application of research on rare events in networked systems. For example, Kumar, Kulkarni, and Santhanam 11 and Bröhl and Lehnertz 12 study the role of network edges in extreme event generation, and Bauzá et al 13 explores the influence of BREXIT on research collaboration networks. A more recent development has been the use of networks as an abstract representation of spatiotemporal data of extreme events; the papers by Wolf et al 14 study rainfall through such an approach.…”

Section: Introductionmentioning

confidence: 99%

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Rare events in complex systems: Understanding and prediction

Malik

Öztürk

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Complex systems such as the earth, brain, human society, and infrastructure systems experience events that are rare in frequency but can have catastrophic consequences. In this Focus Issue, we discuss different tools from network science and dynamical systems that could improve our understanding of rare events in complex systems and provide us with the ability to predict and mitigate their unpleasant outcomes. This collection of articles covers a wide range of topics, such as identifying rare transitions in time series, employing time-delay feedback to mitigate extreme events and network analysis of large-scale spatiotemporal data sets of extreme events. In terms of applications, this Focus Issue includes a wide variety of studies as well, ranging from analysis of a rare political event such as the BREXIT to identifying the cause for the sudden demise of an ancient civilization.

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Section: A Network Science Based Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rare events in complex systems: Understanding and prediction

Malik

Öztürk

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Researchers’ transfer network reveals the evolution of national science and technology capabilities

Yang

Zhao

Zhang

et al. 2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Science and technology capability refers to the comprehensive capability of all factors that affect the development of science and technology, mainly referring to human and material factors related to science and technology, among which human resources are the foundation and driving force. Therefore, researchers become a unique research perspective for the evaluation of national science and technology capabilities. Taking the integrated circuit field as the analysis case, this article proposed a researchers’ transfer network model based on the online open source literature database. From the published literature information, the model obtains the researchers’ transfer network that has a core-periphery structure. The core nodes are the European Union, the United States, China, etc., and these nodes are the most closely connected. A country/region role evolution model is also proposed, which reveals the characteristics of the role evolution of the European Union, the United States, China, and other countries from the perspective of researchers’ transfer, especially their transfer between countries.

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Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions

Mingueza

Ruiz-Manzanares

Gómez‐Gardeñes

et al. 2023

Journal of Complex Networks

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Community detection theory is vital for the structural analysis of many types of complex networks, especially for human-like collaboration networks. In this work, we present a new community detection algorithm, the Targeted Community Merging algorithm, based on the well-known Girvan–Newman algorithm, which allows obtaining community partitions with high values of modularity and a small number of communities. We then perform an analysis and comparison between the departmental and community structure of scientific collaboration networks within the University of Zaragoza. Thus, we draw valuable conclusions from the inter- and intra-departmental collaboration structure that could be useful to take decisions on an eventual departmental restructuring.

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Analyzing the potential impact of BREXIT on the European research collaboration network

Cited by 4 publications

References 50 publications

Rare events in complex systems: Understanding and prediction

Rare events in complex systems: Understanding and prediction

Researchers’ transfer network reveals the evolution of national science and technology capabilities

Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions

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