Graph drawing by force-directed placement
Software—Practice & Experience
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
A Multilevel Algorithm for Force-Directed Graph Drawing
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
A Fast Adaptive Layout Algorithm for Undirected Graphs
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
Interactive Visualization of Small World Graphs
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Geometry-Based Edge Clustering for Graph Visualization
IEEE Transactions on Visualization and Computer Graphics
On the Visualization of Social and other Scale-Free Networks
IEEE Transactions on Visualization and Computer Graphics
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
Visualizing large and clustered networks
GD'06 Proceedings of the 14th international conference on Graph drawing
SDE: graph drawing using spectral distance embedding
GD'05 Proceedings of the 13th international conference on Graph Drawing
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Graphs are central representations of information in many domains including biological and social networks. Graph visualization is needed for discovering underlying structures or patterns within the data, for example communities in a social network, or interaction patterns between protein complexes. Existing graph visualization methods, however, often fail to visualize such structures, because they focus on local details rather than global structural properties of graphs. We suggest a novel modeling-driven approach to graph visualization: As usually in modeling, choose the (generative) model such that it captures what is important in the data. Then visualize similarity of the graph nodes with a suitable multidimensional scaling method, with similarity given by the model; we use a multidimensional scaling method optimized for a rigorous visual information retrieval task. We show experimentally that the resulting method outperforms existing graph visualization methods in finding and visualizing global structures in graphs.