TY - GEN
T1 - Network quantification despite biased labels
AU - Tang, Lei
AU - Gao, Huiji
AU - Liu, Huan
PY - 2010
Y1 - 2010
N2 - The increasing availability of participatory web and social media presents enormous opportunities to study human relations and collective behaviors. Many applications involving decision making want to obtain certain generalized properties about the population in a network, such as the proportion of actors given a category, instead of the category of individuals. While data mining and machine learning researchers have developed many methods for link-based classification or relational learning, most are optimized to classify individual nodes in a network. In order to accurately estimate the prevalence of one class in a network, some quantification method has to be used. In this work, two kinds of approaches are presented: quantification based on classification or quantification based on link analysis. Extensive experiments are conducted on several representative network data, with interesting findings reported concerning efficacy and robustness of different quantification methods, providing insights to further quantify the ebb and flow of online collective behaviors at macro-level.
AB - The increasing availability of participatory web and social media presents enormous opportunities to study human relations and collective behaviors. Many applications involving decision making want to obtain certain generalized properties about the population in a network, such as the proportion of actors given a category, instead of the category of individuals. While data mining and machine learning researchers have developed many methods for link-based classification or relational learning, most are optimized to classify individual nodes in a network. In order to accurately estimate the prevalence of one class in a network, some quantification method has to be used. In this work, two kinds of approaches are presented: quantification based on classification or quantification based on link analysis. Extensive experiments are conducted on several representative network data, with interesting findings reported concerning efficacy and robustness of different quantification methods, providing insights to further quantify the ebb and flow of online collective behaviors at macro-level.
KW - Classification-based quantification
KW - Link-based quantification
KW - Network quantification
KW - Prevalence estimation
UR - http://www.scopus.com/inward/record.url?scp=77956256748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956256748&partnerID=8YFLogxK
U2 - 10.1145/1830252.1830271
DO - 10.1145/1830252.1830271
M3 - Conference contribution
AN - SCOPUS:77956256748
SN - 9781450302142
T3 - Proceedings of the 8th Workshop on Mining and Learning with Graphs, MLG'10
SP - 147
EP - 154
BT - Proceedings of the 8th Workshop on Mining and Learning with Graphs, MLG'10
T2 - 8th Workshop on Mining and Learning with Graphs, MLG'10
Y2 - 24 July 2010 through 25 July 2010
ER -