A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks

Gaskó, Noémi; Lung, Rodica Ioana; Suciu, Mihai Alexandru

doi:10.1007/s11192-016-1968-4

Cited by 19 publications

(11 citation statements)

References 55 publications

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“…It is also worth noting that our research confirms, in line with previous research 20 , 52 , that the alternative representation of collaboration networks, where hyperlinks connect publications instead of coauthors, yield a better representation in that for these type of collaboration networks all topological features eventually mature as layers are coalesced, whilst in the classical representation some topological feature never mature.…”

Section: Discussionsupporting

confidence: 90%

Multilayer representation of collaboration networks with higher-order interactions

Vasilyeva

Козлов

Alfaro-Bittner

et al. 2021

Sci Rep

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Collaboration 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.

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Section: Discussionsupporting

confidence: 90%

Multilayer representation of collaboration networks with higher-order interactions

Vasilyeva

Козлов

Alfaro-Bittner

et al. 2021

Sci Rep

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“…Which may denote that, "Barabasi, A" has co-authored with researchers who belong to the same institute or in the same research field. Whereas "Jeong, H" has co-authored with multiple groups of linked coauthors who may come from different institutes or from different research field which also supports the findings by [11,28].…”

Section: Largest Community Identified By Infomap Algorithmsupporting

confidence: 66%

“…In addition to that, Abbasi [1] also used ego-networks to analyse co-authorship network. Gasko [11] proposed to use clustering methods to identify groups within a network and measures the authors in relation to their collaboration network. Gasko also set weight to show the number of paper published together by the authors while degree shows the numbers of collaboration.…”

Section: Ego Networkmentioning

confidence: 99%

Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

Magalingam¹,

Samy²,

Maarop³

et al. 2018

IJET

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This paper contributes in understanding and gaining meaningful insight about the relationship among the scientist in the co-authorship network using social network analysis. We argue that the relationship analysis is not always a straightforward process. In the past one single measure, for example, the egocentric or centrality measure was used to describe the scientific collaboration patterns separately. In this paper, various analysis such as centrality analysis, ego network, community detection, largest clique and word frequency have been used to examine and interpret the collaboration among the authors. This research is not dominated by known researchers but involves an overall exploration of the network. Our research is mainly guided by the creation of research issues, assessing the type of dataset and the objectives for presenting the co-authorship relationships. It is important to identify the motive of the selected measures in order to achieve the predefined objective. Specific methodology and procedures are designed to solve each research issue respectively. This study reveals that the network interpretation should not be solely based on one network measure, but an explorative analysis results need to be considered because it allows exploring the hidden information through the changes in the network structure, topology patterns and nodes’ position.

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“…Co-authorship CANs are constructed from a list of published papers by considering authors as connected nodes if they co-authored one or more papers together (Logan and Shaw, 1991). Over the past decade, the number of articles focusing on CANs has increased rapidly (Gaskó et al, 2016;Kumar, 2015). CANs have recently been used to study different aspects of research collaboration by extracting various types of information.…”

Section: Relevant Literaturesmentioning

confidence: 99%

A typology of collaborative research networks

Hayat

2017

OIR

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Purpose Many studies have investigated how the structure of the collaborative networks of researchers influences the nature of their work, and its outcome. Co-authorship networks (CANs) have been widely looked at as proxies that can help bring understanding to the structure of research collaborative ties. The purpose of this paper is to provide a framework for describing what influences the formation of different research collaboration patterns. Design/methodology/approach The authors use social network analysis (SNA) to analyze the co-authorship ego networks of the ten most central authors in 24 years of papers (703 papers and 1,118 authors) published in the Proceedings of CASCON, a computer science conference. In order to understand what lead to the formation of the different CANs the authors examined, the authors conducted semi-structured interviews with these authors. Findings Based on this examination, the authors propose a typology that differentiates three styles of co-authorship: matchmaking, brokerage, and teamwork. The authors also provide quantitative SNA-based measures that can help place researchers’ CAN into one of these proposed categories. Given that many different network measures can describe the collaborative network structure of researchers, the authors believe it is important to identify specific network structures that would be meaningful when studying research collaboration. The proposed typology can offer guidance in choosing the appropriate measures for studying research collaboration. Originality/value The results presented in this paper highlight the value of combining SNA analysis with interviews when studying CAN. Moreover, the results show how co-authorship styles can be used to understand the mechanisms leading to the formation of collaborative ties among researchers. The authors discuss several potential implications of these findings for the study of research collaborations.

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A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks

Cited by 19 publications

References 55 publications

Multilayer representation of collaboration networks with higher-order interactions

Multilayer representation of collaboration networks with higher-order interactions

Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

A typology of collaborative research networks

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