01970nas a2200193   4500008004100000022001400041245010300055210006900158260001200227300000700239490000600246520137200252100002101624700002301645700001901668700002001687700002101707856004801728       2016                        eng d  a1869-546900aA supervised approach for intra-/inter-community interaction prediction in dynamic social networks0 asupervised approach for intraintercommunity interaction predicti  c09/2016  a860 v63 aDue 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 intercontinental 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 tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits 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 time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aMiliou, Ioanna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1007/s13278-016-0397-y