1,172 research outputs found
Fostering national research networks: The case of Turkish coauthorship patterns in the social sciences
We analyse the coauthorship networks of researchers affiliated at universities in Turkey by using two databases: the international SSCI database and the Turkish ULAKBIM database. We find that coauthorship networks are composed largely of isolated groups, permitting little knowledge diffusion. Moreover, there seems to be two disparate populations of researchers. While some scholars publish mostly in the international journals, others target the national audience, and there is very little intersection between the two populations. The same observation is valid for universities, among which there is very little collaboration. Our results point out that while Turkish social sciences and humanities publications have been growing impressively in the last decade, domestic networks to ensure the dissemination of knowledge and of research output are very weak and should be supported by domestic policies.Research collaboration, coauthorship, networks, research policy.
Predicting Scientific Success Based on Coauthorship Networks
We address the question to what extent the success of scientific articles is
due to social influence. Analyzing a data set of over 100000 publications from
the field of Computer Science, we study how centrality in the coauthorship
network differs between authors who have highly cited papers and those who do
not. We further show that a machine learning classifier, based only on
coauthorship network centrality measures at time of publication, is able to
predict with high precision whether an article will be highly cited five years
after publication. By this we provide quantitative insight into the social
dimension of scientific publishing - challenging the perception of citations as
an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia
Exploring cooperative game mechanisms of scientific coauthorship networks
Scientific coauthorship, generated by collaborations and competitions among
researchers, reflects effective organizations of human resources. Researchers,
their expected benefits through collaborations, and their cooperative costs
constitute the elements of a game. Hence we propose a cooperative game model to
explore the evolution mechanisms of scientific coauthorship networks. The model
generates geometric hypergraphs, where the costs are modelled by space
distances, and the benefits are expressed by node reputations, i. e. geometric
zones that depend on node position in space and time. Modelled cooperative
strategies conditioned on positive benefit-minus-cost reflect the spatial
reciprocity principle in collaborations, and generate high clustering and
degree assortativity, two typical features of coauthorship networks. Modelled
reputations generate the generalized Poisson parts and fat tails appeared in
specific distributions of empirical data, e. g. paper team size distribution.
The combined effect of modelled costs and reputations reproduces the
transitions emerged in degree distribution, in the correlation between degree
and local clustering coefficient, etc. The model provides an example of how
individual strategies induce network complexity, as well as an application of
game theory to social affiliation networks
Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS
The features of collaboration patterns are often considered to be different
from discipline to discipline. Meanwhile, collaborating among disciplines is an
obvious feature emerged in modern scientific research, which incubates several
interdisciplines. The features of collaborations in and among the disciplines
of biological, physical and social sciences are analyzed based on 52,803 papers
published in a multidisciplinary journal PNAS during 1999 to 2013. From those
data, we found similar transitivity and assortativity of collaboration patterns
as well as the identical distribution type of collaborators per author and that
of papers per author, namely a mixture of generalized Poisson and power-law
distributions. In addition, we found that interdisciplinary research is
undertaken by a considerable fraction of authors, not just those with many
collaborators or those with many papers. This case study provides a window for
understanding aspects of multidisciplinary and interdisciplinary collaboration
patterns
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