scholarly journals Multilayer representation of collaboration networks with higher-order interactions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
E. Vasilyeva ◽  
A. Kozlov ◽  
K. Alfaro-Bittner ◽  
D. Musatov ◽  
A. M. Raigorodskii ◽  
...  

AbstractCollaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.


Physics Today ◽  
2018 ◽  
Vol 71 (2) ◽  
pp. 72-72
Author(s):  
Richard J. Fitzgerald

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2067 ◽  
Author(s):  
Francisco Montoya ◽  
Raul Baños ◽  
Alfredo Alcayde ◽  
Maria Montoya ◽  
Francisco Manzano-Agugliaro

Power quality is a research field related to the proper operation of devices and technological equipment in industry, service, and domestic activities. The level of power quality is determined by variations in voltage, frequency, and waveforms with respect to reference values. These variations correspond to different types of disturbances, including power fluctuations, interruptions, and transients. Several studies have been focused on analysing power quality issues. However, there is a lack of studies on the analysis of both the trending topics and the scientific collaboration network underlying the field of power quality. To address these aspects, an advanced model is used to retrieve data from publications related to power quality and analyse this information using a graph visualisation software and statistical tools. The results suggest that research interests are mainly focused on the analysis of power quality problems and mitigation techniques. Furthermore, they are observed important collaboration networks between researchers within and across countries.


2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton Lopes Martins ◽  
Sueli Mara Soares Pinto Ferreira

Resumo O entendimento das causas e as principais razões que influenciam o modo como os pesquisadores se articulam e constroem suas redes de colaboração científica ainda é uma questão em aberto na pesquisa acadêmica. De fundamental importância para o desenvolvimento de novos indicadores e modos de avaliação da produção científica, o conceito de redes sociais permite operar novos planos de análise, contribuindo com seus aspectos estruturais e dinâmicos ao estudo dos mecanismos e gatilhos causais que levam à constituição dessas redes de colaboração científica. A obtenção de atributos individuais dos pesquisadores, de dados de constituição das redes ao longo do tempo e o modo de desambiguação dos nomes que compõem essas redes de colaboração têm se mostrado os principais desafios de estudos das redes. O objetivo deste artigo é descrever como concebemos uma maneira de estudar as redes de colaboração de uma universidade, com foco específico na Universidade de São Paulo, identificando suas principais estratégias de conectividade e mecanismos causais, além de encontrar as relações entre suas redes e diferentes níveis de produtividade científica de seus participantes. Vale frisar que o artigo apenas descreve as questões da pesquisa e o modo de tratá-las, ficando sua execução para os próximos passos deste trabalho de pesquisa. Para tanto, pretende utilizar como base de análise uma Biblioteca de Produção Científica Institucional em desenvolvimento pelo SiBi/USP, que coleta os artigos publicados por membros da universidade em bases de dados de indexação de revistas nacionais e internacionais, tais como Scielo, Web of Science e BioMed, além da utilização da base de dados institucional para obtenção dos atributos individuais dos pesquisadores participantes dessas redes de colaboração.Palavras-chave análise de redes sociais, indicadores, cientometria, modelos causais.Abstract The understanding of the causes that influence how researchers articulate and build their scientific collaboration networks is still an open question in academic research. Of fundamental importance for the development of new indicators and methods of evaluation of scientific literature, the concept of social networking helps operate new levels of analysis, contributing their structural and dynamic aspects to the study of causal mechanisms and triggers that lead to the formation of these networks of scientific collaboration. Obtaining attributes of individual researchers, data on the constitution of networks over time and mode of disambiguation of the names that make up these collaboration networks have been the main challenges in the area of research networks. The purpose of this article is to describe how we designed a way to study a university’s collaboration networks, focusing on the University of São Paulo, and identifying their key strategies, connectivity and causal mechanisms, as well as finding links between their networks and different levels of participants’ productivity. It should be noted that this article only describes the research questions and how to treat them, leaving their implementation to the next steps of this research. The database used for analysis was the Institutional Scientific Production being developed by Sibi/USP, which collects articles published by members of the university indexed in national and international databases such as Scielo, Web of Science and BioMed, as well as an institutional database to obtain the individual attributes of the researchers participating in these networks.Keywords social network analysis, indicators, scientometrics, causal model


Author(s):  
Cristian K. dos Santos ◽  
Maurcio Onoda ◽  
Victor S. Bursztyn ◽  
Valeria M. Bastos ◽  
Marcello P. A. Fonseca ◽  
...  

2020 ◽  
Author(s):  
Yuanzhi Li ◽  
Margaret M Mayfield ◽  
Bin Wang ◽  
Junli Xiao ◽  
Kamil Kral ◽  
...  

Abstract It is known that biotic interactions are the key to species coexistence and maintenance of species diversity. Traditional studies focus overwhelmingly on pairwise interactions between organisms, ignoring complex higher-order interactions (HOIs). In this study, we present a novel method of calculating individual-level HOIs for trees, and use this method to test the importance of size- and distance-dependent individual-level HOIs to tree performance in a 25-ha temperate forest dynamic plot. We found that full HOIs-inclusive models improved our ability to model and predict the survival and growth of trees, providing empirical evidence that HOIs strongly influence tree performance in this temperate forest. Specifically, assessed HOIs mitigate the competitive direct effects of neighbours on survival and growth of focal trees. Our study lays a foundation for future investigations of the prevalence and relative importance of HOIs in global forests and their impact on species diversity.


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