Fast algorithms for finding nearest common ancestors
SIAM Journal on Computing
Navigating hierarchically clustered networks through fisheye and full-zoom methods
ACM Transactions on Computer-Human Interaction (TOCHI)
Empirical Evaluation of Aesthetics-based Graph Layout
Empirical Software Engineering
IEEE Internet Computing
Which Aesthetic has the Greatest Effect on Human Understanding?
GD '97 Proceedings of the 5th International Symposium on Graph Drawing
Cognitive measurements of graph aesthetics
Information Visualization
The level ancestor problem simplified
Theoretical Computer Science - Latin American theorotical informatics
Interactive Visualization of Small World Graphs
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Elastic Hierarchies: Combining Treemaps and Node-Link Diagrams
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
ASK-GraphView: A Large Scale Graph Visualization System
IEEE Transactions on Visualization and Computer Graphics
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph Visualization Techniques for Web Clustering Engines
IEEE Transactions on Visualization and Computer Graphics
How to Draw ClusteredWeighted Graphs using a Multilevel Force-Directed Graph Drawing Algorithm
IV '07 Proceedings of the 11th International Conference Information Visualization
A Space Efficient Clustered Visualization of Large Graphs
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
GrouseFlocks: Steerable Exploration of Graph Hierarchy Space
IEEE Transactions on Visualization and Computer Graphics
A layout algorithm for undirected compound graphs
Information Sciences: an International Journal
A hybrid space-filling and force-directed layout method for visualizing multiple-category graphs
PACIFICVIS '09 Proceedings of the 2009 IEEE Pacific Visualization Symposium
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multiscale visualization of small world networks
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
An advanced network visualization system for financial crime detection
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
Visual Analysis of Large Graphs Using (X,Y)-Clustering and Hybrid Visualizations
IEEE Transactions on Visualization and Computer Graphics
Drawing large graphs with a potential-field-based multilevel algorithm
GD'04 Proceedings of the 12th international conference on Graph Drawing
Visualizing large graphs with compound-fisheye views and treemaps
GD'04 Proceedings of the 12th international conference on Graph Drawing
Visualizing large hierarchically clustered graphs with a landscape metaphor
GD'12 Proceedings of the 20th international conference on Graph Drawing
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The visual analysis of large and complex relational data sets is a growing need in many application domains, such as social sciences, biology, computer networks, and software engineering. In this respect, the capability of quickly computing two-dimensional layouts of hierarchically clustered networks plays an important role and should be a major requirement of many graph visualization systems. We present algorithmic and experimental advances on the subject, namely: (i) we propose a new drawing algorithm that combines space-filling and fast force-directed methods; it runs in O(n logn+m) time, where n and m are the number of vertices and edges of the network, respectively. This running time does not depend on the number of clusters, thus the algorithm guarantees good time performance independently of the structure of the cluster hierarchy. As a further advantage, the algorithm can be easily parallelized. (ii) We discuss the results of an experimental analysis aimed at understanding which clustering algorithms can be used in combination with our visualization technique to generate better quality drawings for small-world and scale-free networks of medium and large size. As far as we know, no previous similar experiments have been done to this aim.