Reordering massive sequence views: Enabling temporal and structural analysis of dynamic networks

IEEE Pacific Visualization Symposium

Volumn , Issue , 2013, Pages 33-40

  Van Den Elzen, Stef ^a,b   Holten, Danny ^a   Blaas, Jorik ^a   Van Wijk, Jarke J ^b  

^a SYNERSCOPE B V   (Netherlands)

^b EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

I.3.3 Computing Methodologies : Computer Graphics Picture Image Generation

Indexed keywords

DYNAMIC NETWORK; GESTALT PRINCIPLES; I.3.3 [COMPUTING METHODOLOGIES]: COMPUTER GRAPHICS - PICTURE/IMAGE GENERATION; STRUCTURAL ASPECTS; TEMPORAL ASPECTS; TEMPORAL PROPERTY; TEMPORAL SHIFTS; TRANSACTION DATA;

VISUALIZATION;

DATA VISUALIZATION;

EID: 84886668740     PISSN: 21658765     EISSN: 21658773     Source Type: Conference Proceeding    
DOI: 10.1109/PacificVis.2013.6596125     Document Type: Conference Paper

References (46)

1. M. Burch, F. Beck, and S. Diehl. Timeline trees: visualizing sequences of transactions in information hierarchies. In Proc. Working Conf. Advanced Visual Interfaces, pages 75-82, New York, USA, 2008. ACM.
2. M. Burch and S. Diehl. Timeradartrees: Visualizing dynamic compound digraphs. Comput. Graph. Forum, 27(3):823-830, 2008.
3. M. Burch, M. Fritz, F. Beck, and S. Diehl. Timespidertrees: A novel visual metaphor for dynamic compound graphs. In IEEE Symp. Visual Languages and Human-Centric Computing, pages 168-175, 2010.
4. M. Burch, M. Hoferlin, and D. Weiskopf. Layered timeradartrees. In Proc. 15th Int. Conf. Information Visualisation, pages 18-25, 2011.
5. M. Burch, C. Vehlow, F. Beck, S. Diehl, and D. Weiskopf. Parallel edge splatting for scalable dynamic graph visualization. IEEE Trans. Vis. Comput. Graphics, 17(12):2344-2353, 2011.
6. M. Burch and D. Weiskopf. Visualizing dynamic quantitative data in hierarchies - timeedgetrees: Attaching dynamic weights to tree edges. In Proc. Int. Conf. Information Visualization Theory and Applications, pages 177-186, 2011.
7. V. Cerný. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J. of Optimization Theory and Applications, 45:41-51, 1985.
8. B. Cornelissen, D. Holten, A. Zaidman, L. Moonen, J. J. van Wijk, and A. van Deursen. Understanding execution traces using massive sequence and circular bundle views. In Proc. 15th IEEE Int. Conf. Program Comprehension, pages 49-58, 2007.
9. W. De Pauw, D. Lorenz, J. Vlissides, and M. Wegman. Execution patterns in object-oriented visualization. In Proc. 4th Conf. Object-Oriented Technologies and Systems, pages 16-16, Berkeley, CA, USA, 1998. USENIX Association.
10. S. Diehl and C. Görg. Graphs, They Are Changing, volume 2528, pages 23-30. Springer-Verlag, 2002.
11. J. Diesner and K. M. Carley. Exploration of communication networks from the enron email corpus. In Proc. Workshop Link Analysis, Counterterrorism and Security, SIAM Int. Conf. Data Mining, pages 21-23, 2005.
12. S. G. Eick and A. Ward. An interactive visualization for message sequence charts. In Proc. 4th Workshop Program Comprehension, pages 2-8, 1996.
13. P. Erdo{combining double acute accent}s and A. Rényi. On random graphs i. Publicationes Mathematicae Debrecen, 6:290, 1959.
14. M. Farrugia, N. Hurley, and A. Quigley. Exploring temporal ego networks using small multiples and tree-ring layouts. In Proc. 4th Int. Conf. Advances in Human Computer Interfaces, 2011.
15. P. Federico, W. Aigner, S. Miksch, F. Windhager, and L. Zenk. A visual analytics approach to dynamic social networks. In Proc. 11th Int. Conf. Knowledge Management and Knowledge Technologies, pages 1-8, New York, NY, USA, 2011. ACM.
16. Y. Frishman and A. Tal. Dynamic drawing of clustered graphs. In Proc. IEEE Symp. Information Visualization, pages 191-198, Washington, DC, USA, 2004. IEEE Computer Society.
17. M. R. Garey, D. S. Johnson, and L. Stockmeyer. Some simplified npcomplete problems. In Proc. 6th Annu. ACM Symp. Theory of computing, pages 47-63, New York, NY, USA, 1974. ACM.
18. E. B. Goldstein. Visual attention. In Sensation and perception, chapter 9. Wadsworth Cengage Learning, 2009.
19. R. Gove, N. Gramsky, R. Kirby, E. Sefer, A. Sopan, C. Dunne, B. Shneiderman, and M. Taieb-Maimon. Netvisia: Heat map & matrix visualization of dynamic social network statistics & content. In Proc. 3th Int. Conf. Social Computing, pages 19-26, 2011.
20. M. Greilich, M. Burch, and S. Diehl. Visualizing the evolution of compound digraphs with timearctrees. Comput. Graph. Forum, 28(3):975-982, 2009.
21. S. Hadlak, H.-J. Schulz, and H. Schumann. In situ exploration of large dynamic networks. IEEE Trans. Vis. Comput. Graphics, 17(12):2334-2343, 2011.
22. Y. Y. Haimes, L. S. Lason, and D. A.Wismer. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Trans. Syst., Man, Cybern.,Syst., 1(3):296-297, 1971.
23. W. K. Hastings. Monte carlo sampling methods using markov chains and their applications. Biometrika, 57:97-109, 1970.
24. D. Holten, B. Cornelissen, and J. J. van Wijk. Trace visualization using hierarchical edge bundles and massive sequence views. In Proc. 4th IEEE Int. Workshop Visualizing Software for Understanding and Analysis, pages 47-54, 2007.
25. ITU-T. Recommendation Z.120: Message Sequence Chart (MSC). ITU-T, Geneva, 1993.
26. D. F. Jerding and J. T. Stasko. The information mural: a technique for displaying and navigating large information spaces. IEEE Symp. Information Visualization, 0:43-50, 1995.
27. D. F. Jerding and J. T. Stasko. The information mural: a technique for displaying and navigating large information spaces. IEEE Trans. Vis. Comput. Graphics, 4(3):257-271, 1998.
28. D. F. Jerding, J. T. Stasko, and T. Ball. Visualizing message patterns in object-oriented program executions. Technical report, Georgia Institute of Technology, 1996. GIT-GVU-96-15.
29. D. F. Jerding, J. T. Stasko, and T. Ball. Visualizing interactions in program executions. In Proc. 19th Int. Conf. Software Engineering, pages 360-370, New York, NY, USA, 1997. ACM.
30. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671-680, 1983.
31. G. Kumar and M. Garland. Visual exploration of complex time-varying graphs. IEEE Trans. Vis. Comput. Graphics, 12(5):805-812, 2006.
32. E. Mäkinen and H. Siirtola. Reordering the reorderable matrix as an algorithmic problem. In Proc. 1st Int. Conf. Theory and Application of Diagrams, pages 453-467, 2000.
33. N. Metropolis, A.W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. Equation of state calculations by fast computing machines. J. of Chemical Physics, 21:1087-1092, 1953.
34. P. Neumann, S. Schlechtweg, and M. S. T. Carpendale. Arctrees: Visualizing relations in hierarchical data. In EuroVis, pages 53-60. Eurographics Association, 2005.
35. M. Pohl, F. Reitz, and P. Birke. As time goes by: integrated visualization and analysis of dynamic networks. In Proc. Working Conf. Advanced Visual Interfaces, pages 372-375, New York, NY, USA, 2008. ACM.
36. D. J. Ram, T. H. Sreenivas, and K. G. Subramaniam. Parallel simulated annealing algorithms. J. Parallel Distrib. Comput., 37(2):207-212, 1996.
37. F. Reitz, M. Pohl, and S. Diehl. Focused animation of dynamic compound graphs. In Proc. 13th Int. Conf. Information Visualisation, pages 679-684, 2009.
38. M. Renieris and S. P. Reiss. Almost: exploring program traces. In Proc. 8th Int. Conf. Information and Knowledge Management, pages 70-77, New York, NY, USA, 1999. ACM.
39. G. Robertson, R. Fernandez, D. Fisher, B. Lee, and J. Stasko. Effectiveness of animation in trend visualization. IEEE Trans. Vis. Comput. Graphics, 14(6):1325-1332, 2008.
40. M. Rosvall and C. T. Bergstrom. Mapping change in large networks. PLoS ONE, 5(1):e8694, 2010.
41. J. Shetty and J. Adibi. The enron email dataset database schema and brief statistical report, 2004.
42. L. Shi, C. Wang, and Z. Wen. Dynamic network visualization in 1.5d. In Proc. IEEE Pacific Visualization Symp., pages 179-186, 2011.
43. R. J. Sternberg, J. Mio, and J. S. Mio. Cognitive psychology. Cengage Learning/Wadsworth, 2008.
44. B. Suman and P. Kumar. A survey of simulated annealing as a tool for single and multiobjective optimization. J. of the Operational Research Society, 57(10):1143-1160, 2006.
45. T. von Landesberger, A. Kuijper, T. Schreck, J. Kohlhammer, J. J. van Wijk, J.-D. Fekete, and D. Fellner. Visual analysis of large graphs: State-of-the-art and future research challenges. 2011.
46. K.-P. Yee, D. Fisher, R. Dhamija, and M. Hearst. Animated exploration of dynamic graphs with radial layout. In IEEE Symp. Information Visualization, pages 43-50, 2001.