%0 Book Section %1 conf/pkdd/TangZT11 %A Tang, Wenbin %A Zhuang, Honglei %A Tang, Jie %B Proceedings of the ECML/PKDD 2011 %D 2011 %E Gunopulos, Dimitrios %E Hofmann, Thomas %E Malerba, Donato %E Vazirgiannis, Michalis %I Springer Berlin Heidelberg %K relationship_mining %P 381-397 %R 10.1007/978-3-642-23808-6_25 %T Learning to Infer Social Ties in Large Networks %U http://dblp.uni-trier.de/db/conf/pkdd/pkdd2011-3.html#TangZT11 %V 6913 %X In online social networks, most relationships are lack of meaning labels (e.g., “colleague” and “intimate friends”), simply because users do not take the time to label them. An interesting question is: can we automatically infer the type of social relationships in a large network? what are the fundamental factors that imply the type of social relationships? In this work, we formalize the problem of social relationship learning into a semi-supervised framework, and propose a Partially-labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social ties. We tested the model on three different genres of data sets: Publication, Email and Mobile. Experimental results demonstrate that the proposed PLP-FGM model can accurately infer 92.7% of advisor-advisee relationships from the coauthor network (Publication), 88.0% of manager-subordinate relationships from the email network (Email), and 83.1% of the friendships from the mobile network (Mobile). Finally, we develop a distributed learning algorithm to scale up the model to real large networks. %@ 978-3-642-23807-9

@incollection{conf/pkdd/TangZT11, abstract = {In online social networks, most relationships are lack of meaning labels (e.g., “colleague” and “intimate friends”), simply because users do not take the time to label them. An interesting question is: can we automatically infer the type of social relationships in a large network? what are the fundamental factors that imply the type of social relationships? In this work, we formalize the problem of social relationship learning into a semi-supervised framework, and propose a Partially-labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social ties. We tested the model on three different genres of data sets: Publication, Email and Mobile. Experimental results demonstrate that the proposed PLP-FGM model can accurately infer 92.7% of advisor-advisee relationships from the coauthor network (Publication), 88.0% of manager-subordinate relationships from the email network (Email), and 83.1% of the friendships from the mobile network (Mobile). Finally, we develop a distributed learning algorithm to scale up the model to real large networks.}, added-at = {2015-05-10T10:53:14.000+0200}, author = {Tang, Wenbin and Zhuang, Honglei and Tang, Jie}, biburl = {https://www.bibsonomy.org/bibtex/2857a9becb60a167ce97abccfd6e5e51e/jiangxiluning}, booktitle = {Proceedings of the ECML/PKDD 2011}, description = {Learning to Infer Social Ties in Large Networks - Springer}, doi = {10.1007/978-3-642-23808-6_25}, editor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis}, interhash = {10257223148a09d56cd1e3f4e9ea426c}, intrahash = {857a9becb60a167ce97abccfd6e5e51e}, isbn = {978-3-642-23807-9}, keywords = {relationship_mining}, language = {English}, pages = {381-397}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2015-05-10T10:53:14.000+0200}, title = {Learning to Infer Social Ties in Large Networks}, type = {Publication}, url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2011-3.html#TangZT11}, volume = 6913, year = 2011 }