Istituto di Scienza e Tecnologie dell'Informazione     
Rossetti G., Guidotti R., Pennacchioli D., Pedreschi D., Giannotti F. Interaction prediction in dynamic networks exploiting community discovery. In: ASONAM'15 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Paris, France, 25-28 August 2015). Proceedings, pp. 553 - 558. IEEE, 2015.
Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.
URL: http://dl.acm.org/citation.cfm?id=2808797.2809401
DOI: 10.1145/2808797.2809401
Subject Link prediction
Community discovery
Time series
H.2.8 Database Applications. Data Mining

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