01921nas a2200169   4500008004100000245003800041210003800079260002400117520140400141100001901545700001901564700002101583700002001604700002101624700002501645856008101670       2015                        eng d00aQuantification in Social Networks0 aQuantification in Social Networks  aParis, FrancebIEEE3 aIn many real-world applications there is a need to monitor the distribution of a population across different classes, and to track changes in this distribution over time. As an example, an important task is to monitor the percentage of unemployed adults in a given region. When the membership of an individual in a class cannot be established deterministically, a typical solution is the classification task. However, in the above applications the final goal is not determining which class the individuals belong to, but estimating the prevalence of each class in the unlabeled data. This task is called quantification. Most of the work in the literature addressed the quantification problem considering data presented in conventional attribute format. Since the ever-growing availability of web and social media we have a flourish of network data representing a new important source of information and by using quantification network techniques we could quantify collective behavior, i.e., the number of users that are involved in certain type of activities, preferences, or behaviors. In this paper we exploit the homophily effect observed in many social networks in order to construct a quantifier for networked data. Our experiments show the effectiveness of the proposed approaches and the comparison with the existing state-of-the-art quantification methods shows that they are more accurate. 1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca1 aSebastiani, Fabrizio  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf