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Advisor(s)
Abstract(s)
Scientific cooperation is one the most important issues to improve the research quality. A multidisciplinary scientific
group connection among different knowledge areas (e.g., engineering, mathematics, sports, sociology and others) can
be a potential factor to build skilled manpower necessary for strong scientific research. Therefore, based on a case study
from RoboCorp, a multidisciplinary group with researchers from several scientific fields, this paper presents the
scientific cooperation between researchers through networking graph theory. These networks are addressed to answer a
broad variety of questions about collaboration patterns, such as the number of papers authors write, with how many
researchers they write and how researchers “connect” to make papers in specific areas. First, a weighted adjacency
matrix is built based on papers published in accordance with international standards (e.g., ISBN, ISSN), in which it is
possible to perceive the connectivity among researchers. Secondly, an easy-to-use MatLab script was developed to
compute the data, thus presenting the scientific networks. Afterwards, in order to further study the subcommunities
inside the research group, a graph partition methodology was used to divide the graph into clusters. Moreover, several
network concepts were used to evaluate the intra and inter-researchers performances as well as the collective
performance of the whole group. Results showed that the research group is integrally connected when considering all
published papers. However, dividing the networks by scientific areas, one can observe that some researchers ‘loses’
their connectivity, i.e., some authors only publishes on specific scientific categories or with specific researchers within
the group.
Description
Keywords
Coauthorship Networks Graph Theory Researchers connectivity Collective Evaluation
