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Towards Time-aware Link Prediction in Evolving Social Networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45644

Tylenda,  Tomasz
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44021

Angelova,  Ralitsa
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44104

Bedathur,  Srikanta
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Tylenda, T., Angelova, R., & Bedathur, S. (2009). Towards Time-aware Link Prediction in Evolving Social Networks. In L. Giles, P. Mitra, I. Perisic, J. Yen, & H. Zhang (Eds.), Proceedings of the 3rd ACM Workshop on Social Network Mining and Analysis. New York, NY: ACM.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1963-3
Zusammenfassung
Prediction of links - both new as well as recurring - in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., \emph{their evolution over time}. In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which is reflects the application of link prediction better than previous approaches.