@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 {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 {17072,
	title = {Designing Semantic Substrates for Visual Network Exploration},
	journal = {Information VisualizationInformation Visualization},
	volume = {6},
	year = {2007},
	month = {2007/12/21/},
	pages = {281 - 300},
	abstract = {A semantic substrate is a spatial template for a network, where nodes are grouped into regions and laid out within each region according to one or more node attributes. This paper shows how users can be given control in designing their own substrates and how this ability leads to a different approach to network data exploration. Users can create a semantic substrate, enter their data, get feedback from domain experts, edit the semantic substrate, and iteratively continue this procedure until the domain experts are satisfied with the insights they have gained. We illustrate this process in two case studies with domain experts working with legal precedents and food webs. Guidelines for designing substrates are provided, including how to locate, size, and align regions in a substrate, which attributes to choose for grouping nodes into regions, how to select placement methods and which attributes to set as parameters of the selected placement method. Throughout the paper, examples are illustrated with NVSS 2.0, the network visualization tool developed to explore the semantic substrate idea.},
	keywords = {data exploration and analysis, Graphical user interfaces, Information Visualization, network visualization, semantic substrate design},
	isbn = {1473-8716, 1473-8724},
	doi = {10.1057/palgrave.ivs.9500162},
	url = {http://ivi.sagepub.com/content/6/4/281},
	author = {Aris,Aleks and 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}
}
@article {17307,
	title = {Network Visualization by Semantic Substrates},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/10//Sept},
	pages = {733 - 740},
	abstract = {Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations},
	keywords = {Automatic control, data visualisation, Data visualization, Displays, Filters, Graphical user interfaces, Information Visualization, information visualization designers, Law, legal citations, Legal factors, legal precedent data, network visualization, NVSS 1.0, scalability, semantic substrate, Terminology, Tunneling, user-defined semantic substrates},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.166},
	author = {Shneiderman, Ben and Aris,A.}
}
