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 ... [Show full abstract] 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.
Key words: Co-authorship Networks; Graph Theory; Researchers connectivity; Collective Evaluation.