SideKnot: Revealing relation patterns for graph visualization

Graph is an intuitive and powerful tool to present relationships between entities. Efficiently visualizing graphs with node-link diagrams is a great challenge due to visual clutter induced by edge crossing and node-edge overlapping. Many edge bundling methods are proposed to disclose high-level edge patterns. Though previous methods can successfully reveal the coarse graph structure, the relation patterns at individual node level can be overlooked. In addition, many edge bundling algorithms are computationally complex to prevent them from scaling up for extremely large graphs. In this paper, we propose SideKnot, an efficient node-based bundling method to cluster and knot edges. SideKnot is light, runs faster than most existing algorithms, and can disclose the relation patterns of an individual node such as its standing in the graph and pattern of relations with its neighbors.

[1]  François Bertault,et al.  A force-directed algorithm that preserves edge-crossing properties , 1999, Inf. Process. Lett..

[2]  Emden R. Gansner,et al.  An open graph visualization system and its applications to software engineering , 2000 .

[3]  Hong Zhou,et al.  Geometry-Based Edge Clustering for Graph Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[4]  M. Sheelagh T. Carpendale,et al.  Edgelens: an interactive method for managing edge congestion in graphs , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[5]  Manojit Sarkar,et al.  Graphical fisheye views , 1994, CACM.

[6]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[7]  Kelly A. Lyons,et al.  Algorithms for Cluster Busting in Anchored Graph Drawing , 1998, J. Graph Algorithms Appl..

[8]  Zhu Han,et al.  Defense of trust management vulnerabilities in distributed networks , 2008, IEEE Communications Magazine.

[9]  Danny Holten,et al.  Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[10]  Nicola Santoro,et al.  Time-varying graphs and dynamic networks , 2010, Int. J. Parallel Emergent Distributed Syst..

[11]  P. Hanrahan,et al.  Flow map layout , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[12]  Hong Zhou,et al.  Energy-Based Hierarchical Edge Clustering of Graphs , 2008, 2008 IEEE Pacific Visualization Symposium.

[13]  Stephen S. Yau,et al.  A Flexible Trust Model for Distributed Service Infrastructures , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[14]  Jean-Daniel Fekete,et al.  Author Manuscript, Published in "sigchi Conference on Human Factors in Computing Systems Topology-aware Navigation in Large Networks , 2022 .

[15]  Emden R. GansnerYifan.  Multilevel Agglomerative Edge Bundling for Visualizing Large Graphs , 2011 .

[16]  Yehuda Koren,et al.  Improved Circular Layouts , 2006, GD.

[17]  Peter Eades,et al.  FADE: Graph Drawing, Clustering, and Visual Abstraction , 2000, GD.

[18]  Romain Bourqui,et al.  3D Edge Bundling for Geographical Data Visualization , 2010, 2010 14th International Conference Information Visualisation.

[19]  Romain Bourqui,et al.  Winding Roads: Routing edges into bundles , 2010, Comput. Graph. Forum.

[20]  G. Chartrand.  Introductory Graph Theory , 1984 .

[21]  Jarke J. van Wijk,et al.  Force‐Directed Edge Bundling for Graph Visualization , 2009, Comput. Graph. Forum.

[22]  Alan J. Dix,et al.  A Taxonomy of Clutter Reduction for Information Visualisation , 2007, IEEE Transactions on Visualization and Computer Graphics.

[23]  Arjan Kuijper,et al.  Visual Analysis of Large Graphs , 2010, Eurographics.

[24]  Sheelagh Carpendale,et al.  Interactive Poster: Using Edge Plucking for Interactive Graph Exploration , 2005 .

[25]  Yifan Hu,et al.  Efficient Node Overlap Removal Using a Proximity Stress Model , 2009, GD.