@conference {14445,
	title = {Co-evolution of social and affiliation networks},
	booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
	series = {KDD {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {1007 - 1016},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of real-world networks related only to node-to-node link formation. We propose a novel model which captures the co-evolution of social and affiliation networks. We provide surprising insights into group formation based on observations in several real-world networks, showing that users often join groups for reasons other than their friends. Our experiments show that the model is able to capture both the newly observed and previously studied network properties. This work is the first to propose a generative model which captures the statistical properties of these complex networks. The proposed model facilitates controlled experiments which study the effect of actors{\textquoteright} behavior on the evolution of affiliation networks, and it allows the generation of realistic synthetic datasets.},
	keywords = {affiliation network, Evolution, graph generator, groups, social network},
	isbn = {978-1-60558-495-9},
	doi = {10.1145/1557019.1557128},
	url = {http://doi.acm.org/10.1145/1557019.1557128},
	author = {Zheleva,Elena and Sharara,Hossam and Getoor, Lise}
}
@conference {14448,
	title = {To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles},
	booktitle = {Proceedings of the 18th international conference on World wide web},
	series = {WWW {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {531 - 540},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information (which is often not hidden) to infer sensitive attributes. The key novel idea is that in addition to friendship links, groups can be carriers of significant information. We show that on several well-known social media sites, we can easily and accurately recover the information of private-profile users. To the best of our knowledge, this is the first work that uses link-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles.},
	keywords = {attribute inference, groups, privacy, social networks},
	isbn = {978-1-60558-487-4},
	doi = {10.1145/1526709.1526781},
	url = {http://doi.acm.org/10.1145/1526709.1526781},
	author = {Zheleva,Elena and Getoor, Lise}
}
