An efficient parallel optimization for co-authorship network analysis

Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency.