SuperGraph Visualization

@article{Rodrigues2006SuperGraphV,
  title={SuperGraph Visualization},
  author={Jos{\'e} Fernando Rodrigues and Agma J. M. Traina and Christos Faloutsos and Caetano Traina},
  journal={Eighth IEEE International Symposium on Multimedia (ISM'06)},
  year={2006},
  pages={227-234}
}
Given a large social or computer network, how can we visualize it, find patterns, outliers, communities? Although several graph visualization tools exist, they cannot handle large graphs with hundred thousand nodes and possibly million edges. Such graphs bring two challenges: interactive visualization demands prohibitive processing power and, even if we could interactively update the visualization, the user would be overwhelmed by the excessive number of graphical items. To cope with this… Expand
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References

SHOWING 1-10 OF 10 REFERENCES
Multilevel Visualization of Clustered Graphs
TLDR
This paper describes some two dimensional plane drawing algorithms for clustered graphs and shows how to extend these algorithms to three dimensional multilevel drawings, and considers two conventions: straight-line convex drawings and orthogonal rectangular drawings. Expand
Navigating hierarchically clustered networks through fisheye and full-zoom methods
Many information structures are represented as two-dimensional networks (connected graphs) of links and nodes. Because these network tend to be large and quite complex, people often perfer to viewExpand
Exploring Large Graphs in 3D Hyperbolic Space
  • T. Munzner
  • Computer Science
  • IEEE Computer Graphics and Applications
  • 1998
TLDR
A software system that explicitly attempts to handle much larger graphs than previous systems and support dynamic exploration rather than final presentation is described and the applicability of this system to goals beyond simple exploration is discussed. Expand
Multilevel Graph Partitioning Schemes
TLDR
A new coarsening heuristic is presented (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of the sizes of the final partition obtained after multilevel refinement. Expand
Efficient and Practical Algorithms for Sequential Modular Decomposition
TLDR
A practical algorithm with an O(n+m?(m,n) time bound and a variant with a linear time bound is given and a unique decomposition of the vertices into nested modules is described. Expand
Mesh Partitioning: A Multilevel Balancing and Refinement Algorithm
TLDR
An enhancement of the Kernighan--Lin partition optimization algorithm which incorporates load-balancing is presented and the resulting algorithm is tested against a different but related state-of-the-art partitioner and shown to provide improved results. Expand
Drawing graphs using modular decomposition
Proceedings of the IEEE International Symposium on Multimedia
  • Proceedings of the IEEE International Symposium on Multimedia
  • 2006
Dynamic Drawing of Clustered Graphs
TLDR
This paper presents an algorithm for drawing a sequence of graphs that contain an inherent grouping of their vertex set into clusters, and introduces several metrics for measuring layout quality of dynamic clustered graphs. Expand
Drawing Clustered Graphs on an Orthogonal Grid