Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781467382731 |
| DOIs | |
| Publication status | Published - 2 Dec 2015 |
| Externally published | Yes |
| Event | IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 - Paris, France Duration: 19 Oct 2015 → 21 Oct 2015 |
Other
| Other | IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 |
|---|---|
| Country | France |
| City | Paris |
| Period | 19/10/15 → 21/10/15 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems and Management
- Information Systems
Cite this
Quantification in social networks. / Milli, Letizia; Monreale, Anna; Rossetti, Giulio; Pedreschi, Dino; Giannotti, Fosca; Sebastiani, Fabrizio.
Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7344845.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quantification in social networks
AU - Milli, Letizia
AU - Monreale, Anna
AU - Rossetti, Giulio
AU - Pedreschi, Dino
AU - Giannotti, Fosca
AU - Sebastiani, Fabrizio
PY - 2015/12/2
Y1 - 2015/12/2
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84962787564&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962787564&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2015.7344845
DO - 10.1109/DSAA.2015.7344845
M3 - Conference contribution
AN - SCOPUS:84962787564
SN - 9781467382731
BT - Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -