Visualization of graph data is incredibly challenging, particularly when it comes to extremely large, scale-free graphs and social networks. A few simple searches on the Web and you will find some mesmerizing and very cool images. Perhaps the most cited...
A. Noack, and R. Rotta. (2008)cite arxiv:0812.4073
Comment: 12 pages, 10 figures, see
http://www.informatik.tu-cottbus.de/~rrotta/ for downloading the graph
clustering software.
L. Tang, X. Wang, and H. Liu. Technical Report, TR10-006, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, 85287, 2010, (2010)
A. Lancichinetti, and S. Fortunato. (2009)cite arxiv:0908.1062
Comment: 12 pages, 8 figures. The software to compute the values of our
general normalized mutual information will be soon available at
http://santo.fortunato.googlepages.com/inthepress2.
S. Fortunato, and C. Castellano. (2007)cite arxiv:0712.2716
Comment: 42 pages, 13 figures. Chapter of Springer's Encyclopedia of
Complexity and System Science.
T. Evans, and R. Lambiotte. (2009)cite arxiv:0903.2181
Comment: 9 pages, 7 figures. Version 2 includes minor changes to text and
references and some improved figures.
A. Java, A. Joshi, and T. Finin. Proceedings of the Tenth Workshop on Web Mining and Web Usage Analysis (WebKDD), ACM, (August 2008)Held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008).
L. Zhao, and M. Zaki. SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data, page 694--705. New York, NY, USA, ACM, (2005)
A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Proc. First International Conference on Semantics And Digital Media Technology (SAMT), volume 4306 of Lecture Notes in Computer Science, page 56-70. Heidelberg, Springer, (December 2006)