@conference {13766,
	title = {Evaluating visual and statistical exploration of scientific literature networks},
	booktitle = {2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
	year = {2011},
	month = {2011/09/18/22},
	pages = {217 - 224},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system{\textquoteright}s usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with details-on-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.},
	keywords = {abstracting, academic literature, action science explorer, automatic clustering, citation analysis, citation network visualization, Communities, Context, custom exploration goal, Data visualization, Databases, Document filtering, document handling, document ranking, easy-to-understand metrics, empirical evaluation, Google, Graphical user interfaces, Information filtering, Information Visualization, Libraries, literature exploration, network statistics, paper filtering, paper ranking, scientific literature network, statistical exploration, summarization technique, user-defined tasks, visual exploration, Visualization},
	isbn = {978-1-4577-1246-3},
	doi = {10.1109/VLHCC.2011.6070403},
	author = {Gove,R. and Dunne,C. and Shneiderman, Ben and Klavans,J. and Dorr, Bonnie J}
}
@article {17180,
	title = {Graph Analytics-Lessons Learned and Challenges Ahead},
	volume = {31},
	year = {2011},
	month = {2011///},
	pages = {18 - 29},
	abstract = {Graph analytics is one of the most influential and important R\&D topics in the visual analytics community. Researchers with diverse backgrounds from information visualization, human-computer interaction, computer graphics, graph drawing, and data mining have pursued graph analytics from scientific, technical, and social approaches. These studies have addressed both distinct and common challenges. Past successes and mistakes can provide valuable lessons for revising the research agenda. In this article, six researchers from four academic and research institutes identify graph analytics{\textquoteright} fundamental challenges and present both insightful lessons learned from their experience and good practices in graph analytics research. The goal is to critically assess those lessons and shed light on how they can stimulate research and draw attention to grand challenges for graph analytics. The article also establishes principles that could lead to measurable standards and criteria for research.},
	keywords = {citation analysis, citespace, Computer Graphics, document analysis, graphics and multimedia, greengrid, jigsaw system, modeling, power grid analysis, semantic substrates, simulation, social networks, text analysis, Visualization},
	isbn = {0272-1716},
	author = {Wong,Pak Chung and Chen,Chaomei and Gorg,Carsten and Shneiderman, Ben and Stasko,John and Thomas,Jim}
}
