Abstract
The growth and popularization of platforms on scientific production has been the subject of several studies, producing relevant analyses of co-authorship behavior among groups of researchers. Researchers and their scientific productions can be analysed as co-authorship social networks, so researchers are linked through common publications. In this context, co-authoring networks can be analysed to find patterns that can describe or characterize them. This work presents the analysis and characterization of co-authorship networks of academic Brazilian graduate programs in computer science. Data from Brazilian researchers were collected and modeled as co-authoring networks among the graduate programs that researchers take part in. Each network topology was analysed with complex network measurements and three proposed qualitative indices that evaluate the publication’s quality. In addition, the co-authorship networks of the computer science graduate programs were characterized in relation to the assessment received by CAPES, which attributes a qualitative grade to the graduate programs in Brazil. The results show the most relevant topological measurements for the program’s characterization and the evaluations received by the programs in different qualitative degrees, relating the main topological patterns of the co-authorship networks and the CAPES grades of the Brazilian graduate programs in computer science.
Highlights
Social networks have attracted a great deal of attention for decades
The first step was to perform a normalization throughout the feature matrix in order to adjust each feature to the range from 0 to 1
The results show that programs with lower evaluations of Coordination of Superior Level Staff Improvement (CAPES) grade have a lower value in this measure
Summary
Social networks have attracted a great deal of attention for decades. Some studies on this topic date as far back as the early 30s and were mostly done by anthropologists and sociologists [1, 2]. With the increasing use of graph theory to represent social constructs [3], the concepts of small-world [4] and scale-free [5] networks, complex networks [6, 7], and their applications in different contexts, social networks have drawn the attention of researchers from diverse disciplines, such as computer science, biology, mathematics, chemistry and physics. One aspect of this research, namely the parallels between social networks and academic collaborations has not gone unnoticed. Research collaboration can be carried out at different.
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