Visualization-Based Support of Hypothesis Verification for Research Survey with Co-authorship Networks.
ABSTRACT This paper examines the effectiveness of a visu- alization system for getting insight into future research activ- ities from co-authorship networks. A co-authorship network is important information when doing a research survey. In particular, there are many requests on survey that relate with researchers' future activities, such as identification of growing researchers and supervisors. In previous paper we proposed a visualization system for co-authorship networks, which provides the function for identifying research areas and that for identifying temporal variation of both network structure and keyword distribution. This paper examines its effectiveness through field trials by test participants. The results are examined as the process of hypothesis verification, which shows that test participants could perform the task even though they had no background knowledge about InfoVis.
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Conference Paper: Understanding research trends in conferences using paperLens.[Show abstract] [Hide abstract]
ABSTRACT: PaperLens is a novel visualization that reveals trends, connections, and activity throughout a conference community. It tightly couples views across papers, authors, and references. PaperLens was developed to visualize 8 years (1995-2002) of InfoVis conference proceedings and was then extended to visualize 23 years (1982-2004) of the CHI conference proceedings. This paper describes how we analyzed the data and designed PaperLens. We also describe a user study to focus our redesign efforts along with the design changes we made to address usability issues. We summarize lessons learned in the process of design and scaling up to the larger set of CHI conference papers.Extended Abstracts Proceedings of the 2005 Conference on Human Factors in Computing Systems, CHI 2005, Portland, Oregon, USA, April 2-7, 2005; 01/2005
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ABSTRACT: The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.IEEE Transactions on Visualization and Computer Graphics 12/2007; 13(6):1302-9. · 1.90 Impact Factor