Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.
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Heer J, Boyd D. Vizster: Visualizing online social networks. In Proc. IEEE Symposium on Information Visualization, Minneapolis, MN, USA, Oct. 2005, pp.32–39.
Huisman M, van Duijn M A J. Software for social network analysis. In Models and Methods in Social Network Analysis, Carrington P J, Scott J, Wasserman S (eds.), Cambridge University Press, 2005, pp.270–316.
Henry N, Fekete J D. MatrixExplorer: A dual-representation system to explore social networks. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 677–684.
Henry N, Fekete J D, McGuffin M. NodeTrix: A hybrid visualization of social networks. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6): 1302–1309.
Tulip Software. http://tulip.labri.fr/, 2011.
Namata G M, Staats B, Getoor L, Shneiderman B. A dualview approach to interactive network visualization. In Proc. the 16th ACM Conference on Information and Knowledge Management, Lisbon, Portugal, Nov. 2007, pp.939–942.
Shen Z, Ma K L, Eliassi-Rad T. Visual analysis of large heterogeneous social networks by semantic and structural abstraction. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(6): 1427–1439.
Perer A, Shneiderman B. Balancing systematic and flexible exploration of social networks. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 693–700.
Shneiderman B, Aris A. Network visualization by semantic substrates. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 733–740.
Li C T, Lin S D. Egocentric information abstraction for heterogeneous social networks. In Proc. International Conference on Advances in Social Network Analysis and Mining, Athens, Greece, Jul. 2009, pp.255–260.
Gloor P A, Krauss J, Nann S, Fischbach K, Schoder D. Web Science 2.0: Identifying trends through semantic social network analysis. In Proc. International Conference on Computational Science and Engineering, Vancouver, Canada, Aug. 2009, pp.215–222.
Velardi P, Navigli R, Cucchiarelli A, D’Antonio F. A new content-based model for social network analysis. In Proc. IEEE International Conference on Semantic Computing, Santa Clara, CA, USA, Aug. 2008, pp.18–25.
Bezerianos A, Chevalier F, Dragicevic P, Elmqvist N, Fekete J D. GraphDice: A system for exploring multivariate social networks. Computer Graphics Forum, 2010, 29(3): 863–872.
Smith M, Giraud-Carrier C, Purser N. Implicit affinity networks and social capital. Information Technology and Management, 2009, 10(2-3): 123–134.
Pretorius A J, van Wijk J J. Visual analysis of multivariate state transition graphs. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 685–692.
Archambault D, Munzner T, Auber D. GrouseFlocks: Steerable exploration of graph hierarchy space. IEEE Transactions on Visualization and Computer Graphics, Aug. 2008, 14(4): 900–913.
Wattenberg M. Visual exploration of multivariate graphs. In Proc. SIGCHI Conference on Human Factors in Computing Systems, Montreal, Canada, April 2006, pp.811–819.
Paulovich F V, Oliveira M C F, Minghim R. The projection explorer: A flexible tool for projection-based multidimensional visualization. In Proc. XX Brazilian Symposium on Computer Graphics and Image Processing, Belo Horizonte, MG, Brazil, Oct. 2007, pp.27–36.
Orkut. http://www.orkut.com/, 2011.
Salton G, Wong A, Yang C S. A vector space model for automatic indexing. Communications of the ACM, 1975, 18(11): 613–620.
Minghim R, Paulovich F V, Lopes A A. Content-based text mapping using multi-dimensional projections for exploration of document collections. In Proc. SPIE Visualization and Data Analysis, San Jose, CA, USA, 2006.
Navarro G. A guided tour to approximate string matching. ACM Computing Surveys, 2001, 33(1): 31–88.
Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing and Management: An International Journal, 1988, 24(5): 513–523.
Telles G P, Minghim R, Paulovich F V. Normalized compression distance for visual analysis of document collections. Computers & Graphics, 2007, 31(3): 327–337.
Paulovich F V, Nonato L G, Minghim R, Levkowitz H. Least square projection: A fast high precision multidimensional projection technique and its application to document mapping. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(3): 564–575.
Brandes U, Pich C. Eigensolver methods for progressive multidimensional scaling of large data. In Lecture Notes in Computer Science 4372, Kaufmann M, Wagner D (eds.), 2007, pp.42–53.
Ingram S, Munzner T, Olano M. Glimmer: Multilevel MDS on the GPU. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(2): 249–261.
Jolliffe I. Principal Component Analysis. New York, NY, USA: Springer, 2002, p.487.
Netlog. http://www.netlog.com/, 2011.
Fruchterman T M J, Reingold E M. Graph drawing by forcedirected placement. Software —Practice & Experience, Nov. 1991, 21(11): 1129–1164.
Zhang Z K, Zhou T, Zhang Y C. Tag-aware recommender systems: A state-of-the-art survey. Journal of Computer Science and Technology, 2011, 26(5): 767–777.
The Collection of Computer Science Bibliographies. http://liinwww.ira.uka.de/bibliography/, 2011.
Analytic Technologies. http://www.analytictech.com/netdraw/netdraw.htm, 2011.
BibTeX. http://www.bibtex.org/, 2011.
Cox T F, Cox A A M. Multidimensional scaling on a sphere. Communications in Statistics — Theory and Methods, 1991, 20(9): 2943–2953.
VisGraph. http://infoserver.lcad.icmc.usp.br/infovis2/Tools, 2011.
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Martins, R.M., Andery, G.F., Heberle, H. et al. Multidimensional Projections for Visual Analysis of Social Networks. J. Comput. Sci. Technol. 27, 791–810 (2012). https://doi.org/10.1007/s11390-012-1265-5
- social network
- visual exploration
- multidimensional visualization