TY - CONF T1 - Quantification in Social Networks T2 - International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015) Y1 - 2015 A1 - Letizia Milli A1 - Anna Monreale A1 - Giulio Rossetti A1 - Dino Pedreschi A1 - Fosca Giannotti A1 - Fabrizio Sebastiani AB - In 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. JF - International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015) PB - IEEE CY - Paris, France UR - http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf ER -