Conference article

Analyzing Multiple Network Centralities with ViNCent

Björn Zimmer
Linnaeus University, School of Computer Science, Physics and Mathematics (DFM), ISOVIS Group, Växjö, Sweden

Ilir Jusufi
Linnaeus University, School of Computer Science, Physics and Mathematics (DFM), ISOVIS Group, Växjö, Sweden

Andreas Kerren
Linnaeus University, School of Computer Science, Physics and Mathematics (DFM), ISOVIS Group, Växjö, Sweden

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Published in: Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden

Linköping Electronic Conference Proceedings 81:12, p. 87-90

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Published: 2012-11-20

ISBN: 978-91-7519-723-4

ISSN: 1650-3686 (print), 1650-3740 (online)


The analysis of multivariate networks is an important task in various application domains; such as social network analysis or biochemistry. In this paper; we address the interactive visual analysis of the results of centrality computations in context of networks. An important analytical aspect is to examine nodes according to specific centrality values and to compare them. We present a tool that combines exploratory data visualization with automatic analysis techniques; such as computing a variety of centrality values for network nodes as well as hierarchical clustering or node reordering based on centrality values. Automatic and interactive approaches are seamlessly integrated in one single tool which provides insight into the importance of an individual node or groups of nodes and allows quantifying the network structure.


E.1 [Data Structures]: —Graphs and networks H.5.2 [Information Interfaces and Presentation]: User Interfaces—Graphical user interfaces (GUI) I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques


[CM11] CORREA C. D.; MA K.-L.: Visualizing social networks. In Social Network Data Analytics; Aggarwal C.; (Ed.). Springer; 2011; pp. 307–326. 88

[DBETT99] DI BATTISTA G.; EADES P.; TAMASSIA R.; TOLLIS I. G.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall; 1999. 87

[DHK06] DWYER T.; HONG S.-H.; KOSCHÜTZKI D.; SCHREIBER F.; XU K.: Visual analysis of network centralities. In Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation (APVis’06) (Darlinghurst; Australia; 2006); Misue K.; Sugiyama K.; Tanaka J.; (Eds.); Australian Computer Society; ACM International Conference Proceeding Series; vol. 164; pp. 189–198. 87; 88

[GPQX07] GÖRG C.; POHL M.; QELI E.; XU K.: Visual Representations. In Human-Centered Visualization Environments (2007); Kerren A.; Ebert A.; Meyer J.; (Eds.); LNCS Tutorial 4417; Springer; pp. 163–230. 87

[HFM07] HENRY N.; FEKETE J.-D.; MCGUFFIN M. J.: Nodetrix: a hybrid visualization of social networks. IEEE Transactions on Visualization and Computer Graphics (IEEE Visualization Conference and IEEE Conference on Information Visualization) Proceedings 13 (2007); 1302–1309. 90

[JKL05] JACOB R.; KOSCHÜTZKI D.; LEHMANN K. A.; PEETERS L.; TENFELDE-PODEHL D.: Algorithms for centrality indices. In Network Analysis; Brandes U.; Erlebach T.; (Eds.). Springer; 2005; pp. 62–82. 87

[JKS06] JUNKER B.; KOSCHUTZKI D.; SCHREIBER F.: Exploration of biological network centralities with CentiBiN. BMC Bioinformatics 7; 1 (2006); 219. 87

[KKZ12] KERREN A.; KÖSTINGER H.; ZIMMER B.: Vincent - visualisation of network centralities. In Proceedings of the International Conference on Information Visualization Theory and Applications (IVAPP ’12) (2012); INSTICC; pp. 703–712. 87; 88; 89; 90

[Knu93] KNUTH D. E.: The Stanford GraphBase: a platform for combinatorial computing. ACM; New York; NY; USA; 1993. 89

[Mir05] MIRKIN B.: Clustering for Data Mining: A Data Recovery Approach. Chapman & Hall/CRC; Boca Raton; FL; USA; 2005. 89

[New10] NEWMAN M. E. J.: Networks: An Introduction. Oxford University Press; 2010. 87

[NL05] NOACK A.; LEWERENTZ C.: A space of layout styles for hierarchical graph models of software systems. In Proceedings of the 2005 ACM symposium on Software visualization (New York; NY; USA; 2005); SoftVis ’05; ACM; pp. 155–164. 90

[PS08] PERER A.; SHNEIDERMAN B.: Systematic yet flexible discovery: guiding domain experts through exploratory data analysis. In Proceedings of the 13th International Conference on Intelligent User Interfaces (New York; NY; USA; 2008); IUI ’08; ACM; pp. 109–118. 88

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