A research group may be considered a social network, which may be modeled by a graph G with k nodes and m edges. Researchers that make up this network can be interpreted as its nodes or actors, and the connections or links between those researchers (represented by co- authored papers) can be considered as its edges. The aim of this study was to measure the reliability of networks considering unreliable nodes or researchers and perfectly reliable edges or connections. Specifically, a statistical analysis based on classical inference to the network reliability was proposed, obtaining the maximum likelihood estimators and confidence intervals for the individual components (researchers) and the co- authorship network; the methodology was applied to a research group of UNESP registered in CNPq; and measures of centrality of nodes were obtained to assist in identifying situations where the insertion of an edge or connection between two researchers of the group could significantly increases the reliability of this co-authorship network. The results showed the usefulness of statistical inference in the context of social networks reliability, noting that the contribution of each researcher is of extreme importance for the maintenance of a research group. It was also found that calculating the reliability of a co-authorship network can be quite exhausting to be executed and that the centrality measures are a viable tool when it intends to increase the reliability of this network.
social networks; researcher groups; graph theory; statistical inference; centrality measures
