There is an increasing attention towards link prediction in complex networks both in physical and computer science communities. Particularly Online Social Networks (OSNs) are
becoming the most popular platforms for information sharing, content creation and communication between users on the Internet. However, most of the research was done considering only a static snapshot of the network and without using relevant information from
other types of activities.
In that direction, the present thesis proposes a novel method for link prediction using temporal information in Stack Overflow with the assistance of interactions from Github. The
developed multilayer network enhanced with temporal interactions is aiming to improve
the performance of the prediction compared to the traditional methods while the design
choices intend to investigate the evolution of the network through time. In the end, the
generalized framework could be used not only to make accurate link prediction that translate to human interactions over time, but also as a tool to characterize the behavior of the
users in the targeted network.