@conference {16974,
	title = {Analyzing (social media) networks with NodeXL},
	booktitle = {Proceedings of the fourth international conference on Communities and technologies},
	series = {C\&$\#$38;T {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {255 - 264},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.},
	keywords = {excel, network analysis, social media, social network, spreadsheet, Visualization},
	isbn = {978-1-60558-713-4},
	doi = {10.1145/1556460.1556497},
	url = {http://doi.acm.org/10.1145/1556460.1556497},
	author = {Smith,Marc A. and Shneiderman, Ben and Milic-Frayling,Natasa and Mendes Rodrigues,Eduarda and Barash,Vladimir and Dunne,Cody and Capone,Tony and Perer,Adam and Gleave,Eric}
}
@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}
}
