The visual display of quantitative information
The visual display of quantitative information
Computer simulation using particles
Computer simulation using particles
Envisioning information
Graph drawing by force-directed placement
Software—Practice & Experience
Graphical fisheye views of graphs
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Points, spheres, and separators: a unified geometric approach to graph partitioning
Points, spheres, and separators: a unified geometric approach to graph partitioning
A parallel algorithm for multilevel graph partitioning and sparse matrix ordering
Journal of Parallel and Distributed Computing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Tree Data Structures for N-Body Simulation
SIAM Journal on Computing
Clustering Algorithms
How to Draw a Planar Clustered Graph
COCOON '95 Proceedings of the First Annual International Conference on Computing and Combinatorics
GD '96 Proceedings of the Symposium on Graph Drawing
GD '96 Proceedings of the Symposium on Graph Drawing
JIGGLE: Java Interactive Graph Layout Environment
GD '98 Proceedings of the 6th International Symposium on Graph Drawing
A Simple and Unified Method for Drawing Graphs: Magnetic-Spring Algorithm
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
A Multi-Scale Algorithm for Drawing Graphs Nicely
WG '99 Proceedings of the 25th International Workshop on Graph-Theoretic Concepts in Computer Science
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Fast Multi-Scale Method for Drawing Large Graphs
A Fast Multi-Scale Method for Drawing Large Graphs
Visualization and Analysis of Web Graphs
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Graph Drawing by High-Dimensional Embedding
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
The Binary Stress Model for Graph Drawing
Graph Drawing
LearnIT: enhanced search and visualization of IT projects
AIMS'10 Proceedings of the Mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security
An integrated modelling, debugging, and visualisation environment for G12
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Human-centered visualization environments
Human-centered visualization environments
An experimental comparison of fast algorithms for drawing general large graphs
GD'05 Proceedings of the 13th international conference on Graph Drawing
Energy-based clustering of graphs with nonuniform degrees
GD'05 Proceedings of the 13th international conference on Graph Drawing
Drawing large graphs with a potential-field-based multilevel algorithm
GD'04 Proceedings of the 12th international conference on Graph Drawing
GD'11 Proceedings of the 19th international conference on Graph Drawing
Drawing Large Graphs by Low-Rank Stress Majorization
Computer Graphics Forum
A nonlinear method for dimensionality reduction of data using reference nodes
Pattern Recognition and Image Analysis
Visualising Reasoning: What ATP Can Learn From CP
Electronic Notes in Theoretical Computer Science (ENTCS)
Visual spam campaigns analysis using abstract graphs representation
Proceedings of the Ninth International Symposium on Visualization for Cyber Security
Visualisation and analysis of large and complex scale-free networks
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
A scalable parallel force-directed graph layout algorithm
EG PGV'08 Proceedings of the 8th Eurographics conference on Parallel Graphics and Visualization
ImPrEd: an improved force-directed algorithm that prevents nodes from crossing edges
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
StreamEB: stream edge bundling
GD'12 Proceedings of the 20th international conference on Graph Drawing
Visual analysis of large-scale network anomalies
IBM Journal of Research and Development
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A fast algorithm(FADE) for the 2D drawing, geometric clustering and multilevel viewing of large undirected graphs is presented. The algorithm is an extension of the Barnes-Hut hierarchical space decomposition method, which includes edges and multilevel visual abstraction. Compared to the original force directed algorithm, the time overhead is O(e + n log n) where n and e are the numbers of nodes and edges. The improvement is possible since the decomposition tree provides a systematic way to determine the degree of closeness between nodes without explicitly calculating the distance between each node. Different types of regular decomposition trees are introduced. The decomposition tree also represents a hierarchical clustering of the nodes, which improves in a graph theoretic sense as the graph drawing approaches a lower energy state. Finally, the decomposition tree provides a mechanism to view the hierarchical clustering on various levels of abstraction. Larger graphs can be represented more concisely, on a higher level of abstraction, with fewer graphics on screen.