Structure Of Scientific Collaboration Networks Research Articles

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

Abstract 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.

Scientific collaboration networks and firm innovation: the contingent impact of a dynamic environment

PurposeThe environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics.Design/methodology/approachUsing a sample of 174 Chinese firms in the new-energy vehicle industry during 2004–2015, the authors applied a random-effects negative binomial modeling approach to model these relationships.FindingsA broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs.Practical implicationsFirms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments.Originality/valueThis study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.

Spatial Structure, Hierarchy and Formation Mechanisms of Scientific Collaboration Networks: Evidence of the Belt and Road Regions

Scientific collaboration has become an important part of the people-to-people exchanges in the Belt and Road initiative, and remarkable progress has been made since 2013. Taking the 65 countries along the Belt and Road (BRI countries) as the research areas and using collaborated Web of Science (WOS) core collection papers to construct an international scientific collaboration matrix, the paper explores the spatial structure, hierarchy and formation mechanisms of scientific collaboration networks of 65 countries along the Belt and Road. The results show that: 1) Beyond the Belt and Road regions (BRI regions), Central & Eastern Europe, China and West Asia & North Africa have formed a situation in which they all have the most external links with other countries beyond BRI regions. China has the dominant role over other BRI countries in generating scientific links. The overall spatial structure has changed to a skeleton structure consisting of many dense regions, such as Europe, North America, East Asia and Oceania. 2) Within the Belt and Road regions, Central & Eastern Europe has become the largest collaboration partner with other sub-regions in BRI countries. The spatial structure of scientific collaboration networks has transformed from the ‘dual core’ composed of China and the Central & Eastern Europe region, to the ‘multi-polarization’ composed of ‘one zone and multi-points’. 3) The hierarchical structure of scientific collaboration networks presents a typical ‘core-periphery’ structure, and changes from ‘single core’ to ‘double cores’. 4) Among the formation mechanisms of scientific collaboration networks, scientific research strength and social proximity play the most important roles, while geographical distance gradually weakens the hindrance to scientific collaboration.

The Scientific Collaboration Networks in University Management in Brazil

The scientific collaboration, externalized through the formation of networks or research groups, has been used by researchers in the processes of production and publication of researches in the various areas of knowledge. On the basis of this, the aim of this article is to identify the structure of scientific collaboration networks in University Management among higher education institutions, based on the institutional links of researchers in this area. In terms of methodological steps, two data collections were done: one in the publications database of the Journal of University Management in Latin America (GUAL) to select the articles of interest in the research, and another in the current database of the Directory of Research Groups (DRG) of the National Council for Scientific and Technological Development (CNPq) to identify the research groups in University Management registered in the Directory. The main results evidenced the predominance of scientific collaboration in the production of research in the area and that the formation of networks is influenced by several factors, such as social aspects and professional aspirations of researchers, interests in sharing resources, as well as the geographical proximity of institutions.

Quintuple helix structure of Sino-Korean research collaboration in science

The Triple Helix Model has been used for science-mapping in research collaboration since the 1980s. As knowledge-producing activities have rapidly expanded and become interrelated, the triple helix framework is limited in uncovering a broader range of stakeholders and multilateral collaborative activities. In this vein, the present study employs the N-tuple Helix Model as a suitable alternative to analyze the structure of scientific collaboration networks beyond university---industry---government (UIG) relations. The networks of N-tuple Helix relations embedded in Sino-Korean research collaboration are examined in terms of five actors, such as universities, industries, governments, hospitals, and Non-Governmental Organizations (NGOs). The results found that the quintuple helical network exists in Sino-Korean research collaboration. While traditional UIG actors play a pivotal role, hospital and NGO sectors emerge as new drivers for knowledge production and innovation. Each sector is significantly associated with the others and plays distinctive roles and functions. The overall findings provide new insight into a possible change in the traditional Triple Helix framework by demonstrating an alternative "five-helix model" of innovation as a new evolving structure and new dynamic of international collaboration in science.

Local bilateral clustering for identifying research topics and groups from bibliographical data

The structure of scientific collaboration networks provides insight on the relationships between people and disciplines. In this paper, we study a bipartite graph connecting authors to publications and extract from it clusters of authors and articles, interpreting the author clusters as research groups and the article clusters as research topics. Visualisations are proposed to ease the interpretation of such clusters in terms of discovering leaders, the activity level, and other semantic aspects. We discuss the process of obtaining and preprocessing the information from scientific publications, the formulation and implementation of the clustering algorithm, and the creation of the visualisations. Experiments on a test data set are presented, using an initial prototype implementation of the proposed modules.

한국전자통신학회 논문지에 나타난 공동연구 네트워크의 구조에 관한 연구

한국전자통신학회 논문지에 나타난 공동연구 네트워크의 구조에 관한 연구

A Preliminary Assessment of Small World Scholarship Networks in Criminology and Criminal Justice

The present study applies small world network analysis to 727 articles from Criminology, Justice Quarterly, and the Journal of Research in Crime and Delinquency to assess scholarship collaboration trends within criminology and criminal justice (CCJ). Findings indicate that CCJ scholars tend to collaborate (versus sole author) on a great number of peer‐reviewed efforts, with several scholars collaborating with well over 10, and as many as 40, unique (non‐redundant) co‐authors. Consistent with the structure of scientific collaboration networks where scientists are separated by short paths of intermediates, Alex R. Piquero is found to be the most collaborative scholar in this sample and also the best center (central vertex) in the collaboration graph by linking to other scholars, on average, by only 3.6 degrees of separation. To further illustrate combinatorial patterns among CCJ scholars, this study also offers a descriptive and graphical analogue to the “Erdös number” from mathematics (here, the “Piquero number”), focusing on co‐authorship branching from first‐tier collaborators. In our view, small world analysis holds promise in better understanding far‐reaching collaboration patterns by the CCJ professoriate.

International Collaboration Networks of Chinese Scientometrics

In the present study, we try to identify the structure of scientific collaboration networks in scientometrics by using bibliographic data of all papers published by Chinese scientometricians in the international journal Scientometrics retrieved from the Science Citation Index (SCI) of the years 1978–2007. The study is based on bibliographic data retrieved from the Web of Science. The data contains all types of documents published by Chinese scientometricians in Scientometrics during 1978 to 2007.Combined analysis of social network analysis (SNA) and cooccurrence analysis is explored to reveal: (1) The structure of the international collaboration network of Chinese scientometrics; (2) The major collaborative fields of the whole network and of different collaborative sub-networks; (3) The collaborative center of the collaboration network in Chinese scientometrics; (4) The difference of the structure of international collaborative networks between China and India.

Scientific Collaboration in Academic Institutions: A Case Study with Indian Institute of Science Publications

The structure of scientific collaboration networks of the publications of the Indian Institute of Science for a span of 11 years from 1996-2006 is investigated using Bibexcel software as an analytical tool for the data derived from the Web of Science database. The study analyses the author productivity, discipline-wise and institution-wise collaboration and national and international collaboration. It is observed from the analysis that large numbers of research publications are collaborative in nature. There is a steady growth in the collaboration during the study period. The top five countries with which IISc is collaborating is: USA, Japan, France, Germany and UK. Major disciplines in which collaboration is observed are: Physics, Chemistry, Biology, Engineering and Materials Science. The top five institutions with which IISc is collaborating are: Jawaharlal Nehru Centre for Advanced Science Research, Indian Institute of Technology, Bhabha Atomic Research Centre, Tata Institute of Fundamental Research and University of Madras.

The structure of scientific collaboration networks in Scientometrics

The structure of scientific collaboration networks in scientometrics is investigated at the level of individuals by using bibliographic data of all papers published in the international journal Scientometrics retrieved from the Science Citation Index (SCI) of the years 1978–2004. Combined analysis of social network analysis (SNA), co-occurrence analysis, cluster analysis and frequency analysis of words is explored to reveal: (1) The microstructure of the collaboration network on scientists’ aspects of scientometrics; (2) The major collaborative fields of the whole network and of different collaborative sub-networks; (3) The collaborative center of the collaboration network in scientometrics.

The structure of scientific collaboration networks.

The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.

From the Cover: The structure of scientific collaboration networks

The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.