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Rossetti G., Guidotti R., Ioanna M., Pedreschi D., Giannotti F. A supervised approach for intra-/inter-community interaction prediction in dynamic social networks. In: Social Network Analysis and Mining, vol. 6 (1) article n. 86. Springer, 2016. [Online First 27 September 2016]
 
 
Abstract
(English)
Due to the growing availability of internet services in the last decade, the interactions between people became more and more easy to establish. For example we can have an inter-continental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario one of the most challenging task involves the prediction of future interactions between couples of actors (i.e. users in online social networks, researchers in collaboration networks, and so on). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real timestamped networks show how our approach is able to reach high accuracy.
URL: http://link.springer.com/article/10.1007/s13278-016-0397-y
DOI: 10.1007/s13278-016-0397-y
Subject Link Prediction
Community Detection
H.2.8 DATABASE MANAGEMENT. Database Applications. Data Mining
68W01


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