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MARUYAMA, William Takahiro. Link Prediction in academic social networks. 2016. 154 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of Sao Paulo, Sao Paulo, 2016. Nowadays, social networks are gaining prominence in the day-to-day lives. In these networks, different relationships are established between entities that share some characteristic or common goal. A huge amount of information about the Brazilian national scientific production can be found in the Lattes Platform, which is a system used to record the curricula of researchers in Brazil. From this information, it is possible to build an academic social network, where relations between researchers represent a partnership in the production of a publication a link. In social network analysis there is a research area known as link prediction, which aims to identify future relationships. This task may facilitate communication among researchers and optimize the scientific production process identifying possible collaborators. This project analyzed the influence of different attributes found in the literature and data filters to predict co-authorship relationships in academic social networks. Was approached two types of problems in predicting relationships, the general problem that analyzes all possible co-authoring relationships and the problem of new co-authoring that relates to novel co-authorships relationships in the network. The experimental results were promising to the prediction general problem, combining attributes and using filters. However, for the new co-authorships problem the results were not as good. The experiments evaluated different strategies and analyzed the costs and benefits of each. We concluded that to deal with the co-authorships prediction problem in academic social networking it is necessary to analyze the advantages and disadvantages among the strategies, finding a balance between the recall of the positive class and the overall accuracy.

Predição de coautorias em redes sociais acadêmicas