Co-evolution of social and affiliation networks Export

@inproceedings{citeulike:5096941, 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' behavior on the evolution of affiliation networks, and it allows the generation of realistic synthetic datasets.}, address = {New York, NY, USA}, author = {Zheleva, Elena and Sharara, Hossam and Getoor, Lise}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {5096941}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557019.1557128}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557019.1557128}, doi = {10.1145/1557019.1557128}, isbn = {978-1-60558-495-9}, keywords = {evolving}, location = {Paris, France}, pages = {1007--1016}, posted-at = {2009-07-21 05:36:53}, priority = {2}, publisher = {ACM}, title = {Co-evolution of social and affiliation networks}, url = {http://dx.doi.org/10.1145/1557019.1557128}, year = {2009} }

TY - CONF ID - citeulike:5096941 L3 - citeulike-article-id:5096941 N2 - 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' behavior on the evolution of affiliation networks, and it allows the generation of realistic synthetic datasets. CY - Paris, France SN - 978-1-60558-495-9 AD - New York, NY, USA EP - 1016 TI - Co-evolution of social and affiliation networks PB - ACM T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 1007 KW - evolving AU - Zheleva, Elena AU - Sharara, Hossam AU - Getoor, Lise PY - 2009/// UR - http://dx.doi.org/10.1145/1557019.1557128 ER -