Choose the best! ranking group of users in collaborative networks

Social Networks analysis is driving both research and industrial effort as the outcomes of this activity are relevant both from a merely theoretical point of view and for the potential market advantages they can provide to companies. Indeed, there is a growing number of applications that call for user (social) intervention with the aim of helping each other in solving complex tasks or rating other users work. The topic is even more intriguing when a reward is given to users that properly complete their tasks. In this paper, we focus on the analysis of user mutual rankings in a collaborative network where they contribute to the solution of complex tasks. We leverage Exponential Random Graph to model user interaction rankings and we evaluate our approach in a real life scenario.