Print Email Facebook Twitter Link Prediction using Temporal Information in Multilayer Networks Title Link Prediction using Temporal Information in Multilayer Networks Author Nikolopoulos, Dionysis (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Hanjalic, A. (graduation committee) Tintarev, N. (graduation committee) Wang, H. (mentor) Degree granting institution Delft University of Technology Programme Computer Science | Multimedia Computing Date 2019-02-20 Abstract There is an increasing attention towards link prediction in complex networks both in physical and computer science communities. Particularly Online Social Networks (OSNs) arebecoming 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 fromother 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. Thedeveloped multilayer network enhanced with temporal interactions is aiming to improvethe performance of the prediction compared to the traditional methods while the designchoices intend to investigate the evolution of the network through time. In the end, thegeneralized 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 theusers in the targeted network. Subject link predictiontemporal networksMachine Learning To reference this document use: http://resolver.tudelft.nl/uuid:a0dbb6da-9a53-4de1-92a0-9cd955486b94 Part of collection Student theses Document type master thesis Rights © 2019 Dionysis Nikolopoulos Files PDF Thesis_5_1_.pdf 1.88 MB Close viewer /islandora/object/uuid:a0dbb6da-9a53-4de1-92a0-9cd955486b94/datastream/OBJ/view