@conference {17181,
	title = {Group-in-a-Box Layout for Multi-faceted Analysis of Communities},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {354 - 361},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.},
	keywords = {Algorithm design and analysis, category based social graph partitions, clustered graphs, clustering, Clustering algorithms, Communities, data visualisation, force-directed, gender, geographic location, graph layout algorithms, graph theory, group-in-a-box, group-in-a-box layout, Image edge detection, Layout, meta-layout, multifaceted community analysis, network subgraph visualization, network visualization, pattern clustering, profession, semantic substrates, Social network services, social networking (online), social networks, treemap space filling technique, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.139},
	author = {Rodrigues,E.M. and Milic-Frayling,N. and Smith,M. and Shneiderman, Ben and Hansen,D.}
}
@conference {17299,
	title = {Motivation for Participation in Online Neighborhood Watch Communities: An Empirical Study Involving Invitation Letters},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {760 - 765},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a three-part experiment designed to investigate the motivations of users of a community safety and neighborhood watch social networking website. The experiment centers around an intervention into the invitation system that current users employ to invite nonmembers to join the site, and involves several versions of an invitation email which differ by expressing one of four possible motivations for using such a site. The research presented investigates how potential users{\textquoteright} choice of whether or not to join the site is affected by the use case presented by the invitation. It also includes an investigation of the motivations of current users of the site, as reported in an online survey. The experiment yielded no significant difference in responses to the emails. Overall, invitations that included a specific motivation slightly outperformed those which did not, but not to a statistically significant degree. We conclude that although users have specific motivations for using the site, as reported in the survey, attempting to increase response rates to invitation emails by suggesting use cases of the site is surprisingly unlikely to be successful.},
	keywords = {Art, Communities, community safety, Electronic mail, Interviews, invitation email, invitations, motivation, neighborhood watch, Online communities, online neighborhood watch communities, online survey, participation, Safety, Security, social media, Social network services, social networking (online), social networking Website},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.108},
	author = {Violi,N. and Shneiderman, Ben and Hanson,A. and Rey,P. J}
}
@conference {17306,
	title = {NetVisia: Heat Map \& Matrix Visualization of Dynamic Social Network Statistics \& Content},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {19 - 26},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Visualizations of static networks in the form of node-link diagrams have evolved rapidly, though researchers are still grappling with how best to show evolution of nodes over time in these diagrams. This paper introduces NetVisia, a social network visualization system designed to support users in exploring temporal evolution in networks by using heat maps to display node attribute changes over time. NetVisia{\textquoteright}s novel contributions to network visualizations are to (1) cluster nodes in the heat map by similar metric values instead of by topological similarity, and (2) align nodes in the heat map by events. We compare NetVisia to existing systems and describe a formative user evaluation of a NetVisia prototype with four participants that emphasized the need for tool tips and coordinated views. Despite the presence of some usability issues, in 30-40 minutes the user evaluation participants discovered new insights about the data set which had not been discovered using other systems. We discuss implemented improvements to NetVisia, and analyze a co-occurrence network of 228 business intelligence concepts and entities. This analysis confirms the utility of a clustered heat map to discover outlier nodes and time periods.},
	keywords = {business intelligence concept, business intelligence entity, competitive intelligence, data visualisation, dynamic networks, dynamic social network, heat map, Heating, Image color analysis, Information Visualization, Layout, matrix visualization, measurement, NetVisia system, network evolution, network visualization, node-link diagrams, outlier node, social network content, Social network services, social network statistics, social networking (online), social networks, static network visualization, time period, topological similarity, Training, usability, user evaluation, User interfaces},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.216},
	author = {Gove,R. and Gramsky,N. and Kirby,R. and Sefer,E. and Sopan,A. and Dunne,C. and Shneiderman, Ben and Taieb-Maimon,M.}
}
@article {17045,
	title = {Cyberinfrastructure for Social Action on National Priorities},
	journal = {Computer},
	volume = {43},
	year = {2010},
	month = {2010/11//},
	pages = {20 - 21},
	abstract = {Extensive research is needed to build upon currently used media and tools to foster wider participation, address national priorities, and deal with potential dangers associated with technology-mediated social participation.},
	keywords = {Collaborative tools, Peer to peer computing, Public policy, Research initiatives, Social network services, Special issues and sections, Technology-mediated social participation},
	isbn = {0018-9162},
	doi = {10.1109/MC.2010.315},
	author = {Pirolli,Peter and Preece,Jenny and Shneiderman, Ben}
}
@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 {17653,
	title = {Rigorous Probabilistic Trust-Inference with Applications to Clustering},
	booktitle = {IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT {\textquoteright}09},
	volume = {1},
	year = {2009},
	month = {2009/09/15/18},
	pages = {655 - 658},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results.},
	keywords = {Clustering algorithms, Conferences, Educational institutions, Extraterrestrial measurements, Inference algorithms, Intelligent agent, random graphs, Social network services, trust inferrence, Visualization, Voting, Web sites},
	isbn = {978-0-7695-3801-3},
	doi = {10.1109/WI-IAT.2009.109},
	author = {DuBois,Thomas and Golbeck,Jennifer and Srinivasan, Aravind}
}
@conference {17453,
	title = {Understanding social computing participation with visual exploration tools},
	booktitle = {International Symposium on Collaborative Technologies and Systems, 2009. CTS {\textquoteright}09},
	year = {2009},
	month = {2009/05/18/22},
	pages = {xi-xii - xi-xii},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The rapid growth of socio-technical systems, social media and social networking websites has raised the importance of understanding the determinants of their success. The pressure to understand success is increased by the shift from playful discretionary applications to mission critical applications in government, business, and civic settings. These include homeland defense, energy sustainability, environmental conservation, disaster response, and community safety. Information visualization tools and statistical methods can both be helpful, but their utility grows when they are well-integrated. This talk will demonstrate novel tools for network evolution and offer a framework for thinking about motivating technology-mediated social participation.},
	keywords = {Application software, Books, Collaborative tools, Computer science, Data visualization, Educational institutions, History, International collaboration, Social network services, Sociotechnical systems},
	isbn = {978-1-4244-4584-4},
	doi = {10.1109/CTS.2009.5067426},
	author = {Shneiderman, Ben}
}
@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}
}
