Interactive temporal display through collaboration networks visualization

Visual analytics play an important role in understanding complex datasets. The bibliographic database is often visualized as a collaboration network to illustrate the connections between researchers. Static networks, however, barely reveal any information when the dataset includes temporal variables. In this article, we propose an embedded network visualization to display the temporal patterns hiding in the data and use intelligent filters to avoid occlusion. We examined different graphing styles, such as the temporal display and the time direction, to find the best way to present the temporal features. In addition, we demonstrate the utility of our approach with case studies and evaluations of real bibliographic databases.

[1]  Xin Tong,et al.  TextFlow: Towards Better Understanding of Evolving Topics in Text , 2011, IEEE Transactions on Visualization and Computer Graphics.

[2]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[3]  Takayuki Itoh,et al.  A Visualization of Research Papers Based on the Topics and Citation Network , 2015, 2015 19th International Conference on Information Visualisation.

[4]  Chaomei Chen,et al.  CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature , 2006, J. Assoc. Inf. Sci. Technol..

[5]  Peter J. Passmore,et al.  A User Study on Curved Edges in Graph Visualization , 2012, IEEE Transactions on Visualization and Computer Graphics.

[6]  Michael Jünger,et al.  An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs , 2005, GD.

[7]  Sándor Soós.  Age-sensitive bibliographic coupling reflecting the history of science: The case of the Species Problem , 2013, Scientometrics.

[8]  Václav Snásel,et al.  Forcoa.NET: An interactive tool for exploring the significance of authorship networks in DBLP data , 2011, 2011 International Conference on Computational Aspects of Social Networks (CASoN).

[9]  Chen Wang,et al.  1.5D Egocentric Dynamic Network Visualization , 2015, IEEE Transactions on Visualization and Computer Graphics.

[10]  Jiawan Zhang,et al.  A text visualization method for cross-domain research topic mining , 2016, J. Vis..

[11]  Yu-Wei Chang,et al.  Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses , 2015, Scientometrics.

[12]  Pierre Dragicevic,et al.  Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data , 2016, IEEE Transactions on Visualization and Computer Graphics.

[13]  Iraklis Varlamis,et al.  Visualizing Bibliographic Databases as Graphs and Mining Potential Research Synergies , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[14]  Ali Daud,et al.  Using machine learning techniques for rising star prediction in co-author network , 2014, Scientometrics.

[15]  Jarke J. van Wijk,et al.  A user study on visualizing directed edges in graphs , 2009, CHI.

[16]  Heidrun Schumann,et al.  Visualization of Time-Oriented Data , 2011, Human-Computer Interaction Series.

[17]  Shigeo Takahashi,et al.  Interactively Uncluttering Node Overlaps for Network Visualization , 2015, 2015 19th International Conference on Information Visualisation.

[18]  Andreas Noack,et al.  Energy Models for Graph Clustering , 2007, J. Graph Algorithms Appl..

[19]  Ye Zhao,et al.  STREAMIT: Dynamic visualization and interactive exploration of text streams , 2011, 2011 IEEE Pacific Visualization Symposium.

[20]  Ying Li,et al.  Author cooperation relationship in digital publishing based on social network analysis , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[21]  Tamara Munzner,et al.  TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text , 2016, IEEE Transactions on Visualization and Computer Graphics.

[22]  Susan Gauch,et al.  Social network analysis for predicting emerging researchers , 2015, 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K).

[23]  Martin Wattenberg,et al.  Studying cooperation and conflict between authors with history flow visualizations , 2004, CHI.

[24]  Yu Liu,et al.  A Bibliographic Analysis and Collaboration Patterns of IEEE Transactions on Intelligent Transportation Systems Between 2000 and 2015 , 2016, IEEE Transactions on Intelligent Transportation Systems.

[25]  Feng Xia,et al.  Mining advisor-advisee relationships in scholarly big data: A deep learning approach , 2016, 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL).

[26]  M. Sheelagh T. Carpendale,et al.  A Review of Temporal Data Visualizations Based on Space-Time Cube Operations , 2014, EuroVis.

[27]  Chaomei Chen,et al.  Visualizing the Intellectual Structure with Paper-Reference Matrices , 2009, IEEE Transactions on Visualization and Computer Graphics.

[28]  Jean-Daniel Fekete,et al.  An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs , 2011, 2011 IEEE Pacific Visualization Symposium.

[29]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Diansheng Guo,et al.  Design and evaluation of line symbolizations for origin–destination flow maps , 2017, Inf. Vis..

[31]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.