@conference {17168,
	title = {First Steps to Netviz Nirvana: Evaluating Social Network Analysis with NodeXL},
	booktitle = {International Conference on Computational Science and Engineering, 2009. CSE {\textquoteright}09},
	volume = {4},
	year = {2009},
	month = {2009/08/29/31},
	pages = {332 - 339},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier-to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sense-making for students of SNA.},
	keywords = {Computer science, computer science education, data visualisation, Data visualization, Educational institutions, graph drawing, graph layout algorithm, Information services, Information Visualization, Internet, Libraries, Microsoft Excel open-source template, MILC, multi-dimensional in-depth long-term case studies, Netviz Nirvana, NodeXL, Open source software, Programming profession, SNA, social network analysis, Social network services, social networking (online), spreadsheet programs, structural relationship, teaching, visual analytics, visualization tool, Web sites},
	isbn = {978-1-4244-5334-4},
	doi = {10.1109/CSE.2009.120},
	author = {Bonsignore,E. M and Dunne,C. and Rotman,D. and Smith,M. and Capone,T. and Hansen,D. L and Shneiderman, Ben}
}
@article {17238,
	title = {Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines},
	journal = {IEEE Computer Graphics and Applications},
	volume = {29},
	year = {2009},
	month = {2009/06//May},
	pages = {39 - 51},
	abstract = {Evaluating visual-analytics systems is challenging because laboratory-based controlled experiments might not effectively represent analytical tasks. One such system, Social Action, integrates statistics and visualization in an interactive exploratory tool for social network analysis. This article describes results from long-term case studies with domain experts and extends established design goals for information visualization.},
	keywords = {case studies, Control systems, Data analysis, data mining, data visualisation, Data visualization, data-mining, design guidelines, Employment, exploration, Filters, Guidelines, Information Visualization, insights, laboratory-based controlled experiments, Performance analysis, social network analysis, Social network services, social networking (online), social networks, SocialAction, statistical analysis, Statistics, visual analytics, visual-analytics systems, Visualization},
	isbn = {0272-1716},
	doi = {10.1109/MCG.2009.44},
	author = {Perer,A. and Shneiderman, Ben}
}
@conference {14450,
	title = {Preserving the privacy of sensitive relationships in graph data},
	booktitle = {Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD},
	series = {PinKDD{\textquoteright}07},
	year = {2008},
	month = {2008///},
	pages = {153 - 171},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data as link reidentification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.},
	keywords = {anonymization, graph data, identification, link mining, noisy-or, privacy, social network analysis},
	isbn = {3-540-78477-2, 978-3-540-78477-7},
	url = {http://dl.acm.org/citation.cfm?id=1793474.1793485},
	author = {Zheleva,Elena and Getoor, Lise}
}
@article {16983,
	title = {Balancing Systematic and Flexible Exploration of Social Networks},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/10//Sept},
	pages = {693 - 700},
	abstract = {Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks},
	keywords = {Aggregates, algorithms, attribute ranking, Cluster Analysis, Computer Graphics, Computer simulation, Coordinate measuring machines, coordinated views, Data analysis, data visualisation, Data visualization, exploratory data analysis, Filters, Gain measurement, graph theory, Graphical user interfaces, Information Storage and Retrieval, interactive graph visualization, matrix algebra, matrix overview, Models, Biological, Navigation, network visualization, Pattern analysis, Population Dynamics, Social Behavior, social network analysis, Social network services, social networks, social sciences computing, Social Support, SocialAction, software, statistical analysis, statistical methods, User-Computer Interface},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.122},
	author = {Perer,A. and Shneiderman, Ben}
}
