@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.}
}
@conference {17442,
	title = {TreeVersity: Comparing tree structures by topology and node{\textquoteright}s attributes differences},
	booktitle = {2011 IEEE Conference on Visual Analytics Science and Technology (VAST)},
	year = {2011},
	month = {2011/10/23/28},
	pages = {275 - 276},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {It is common to classify data in hierarchies, they provide a comprehensible way of understanding big amounts of data. From budgets to organizational charts or even the stock market, trees are everywhere and people find them easy to use. However when analysts need to compare two versions of the same tree structure, or two related taxonomies, the task is not so easy. Much work has been done on this topic, but almost all of it has been restricted to either compare the trees by topology, or by the node attribute values. With this project we are proposing TreeVersity, a framework for comparing tree structures, both by structural changes and by differences in the node attributes. This paper is based on our previous work on comparing traffic agencies using LifeFlow [1, 2] and on a first prototype of TreeVersity.},
	keywords = {Computer science, data classification, Data visualization, Educational institutions, hierarchy, Image color analysis, LifeFlow, node attributes differences, Pattern classification, structural changes, Topology, topology attributes differences, traffic agencies, tree structures comparison, trees (mathematics), TreeVersity, Vegetation, Visualization},
	isbn = {978-1-4673-0015-5},
	doi = {10.1109/VAST.2011.6102471},
	author = {Gomez,J.A.G. and Buck-Coleman,A. and Plaisant, Catherine and Shneiderman, Ben}
}
