The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - July-Aug. (2013 vol.33)
pp: 88-96
ABSTRACT
The prevailing choices to graphically represent a social network are a node-link graph and an adjacency matrix. Both techniques have unique strengths and weaknesses for different domain applications. This article focuses on how to change adjacency matrices from merely showing pairwise associations among network actors (or graph nodes) to depicting clusters of a social network. Node-link graphs supplement the discussion.
INDEX TERMS
Social network services, Data visualization, Visualization, Image color analysis, Visual analytics, Geospatial analysis, Sociology,computer graphics, adjacency matrix, node-link graph, social networks, visualization, visual analytics
CITATION
Pak Chung Wong, P. Mackey, H. Foote, R. May, "Visual Matrix Clustering of Social Networks", IEEE Computer Graphics and Applications, vol.33, no. 4, pp. 88-96, July-Aug. 2013, doi:10.1109/MCG.2013.66
REFERENCES
1. W.S. Cleveland, Visualizing Data, Hobart Press, 1993.
2. C. Mueller, B. Martin, and A. Lumsdaine, “A Comparison of Vertex Ordering Algorithms for Large Graph Visualization,” Proc. 6th Int'l Asia-Pacific Symp. Visualization (APVIS 07), IEEE CS, 2007.
3. P.C. Wong et al., “A Space-Filling Visualization Technique for Multivariate Small-World Graphs,” IEEE Trans. Visualization and Computer Graphics, vol. 18, no. 4, 2012 pp. 797-809.
37 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool