<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Diego Pennacchioli</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interaction Prediction in Dynamic Networks exploiting Community Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">International conference on Advances in Social Network Analysis and Mining, ASONAM 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?doid=2808797.2809401</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Paris, France</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4503-3854-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>