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Volume 27 Issue 4
July  2012
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Rafael Messias Martins, Gabriel Faria Andery, Henry Heberle, Fernando Vieira Paulovich, Alneu de Andrade Lopes, Helio Pedrini, Rosane Minghim. Multidimensional Projections for Visual Analysis of Social Networks[J]. Journal of Computer Science and Technology, 2012, 27(4): 791-810. DOI: 10.1007/s11390-012-1265-5
Citation: Rafael Messias Martins, Gabriel Faria Andery, Henry Heberle, Fernando Vieira Paulovich, Alneu de Andrade Lopes, Helio Pedrini, Rosane Minghim. Multidimensional Projections for Visual Analysis of Social Networks[J]. Journal of Computer Science and Technology, 2012, 27(4): 791-810. DOI: 10.1007/s11390-012-1265-5
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Multidimensional Projections for Visual Analysis of Social Networks

  • 1. Institute of Mathematics and Computer Science, University of S?ao Paulo, S?ao Carlos 13566-590, Brazil;
    2. CPM Braxis Capgemini, S?ao Paulo 04543-900, Brazil;
    3. Institute of Computing, University of Campinas, Campinas-SP 13083-852, Brazil

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  • Received Date: March 14, 2011
  • Revised Date: January 11, 2012
  • Published Date: July 04, 2012
    Abstract
  • 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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