On the optimal layout of planar graphs with fixed boundary
SIAM Journal on Computing
Algorithms for clustering data
Algorithms for clustering data
Automatic graph drawing and readability of diagrams
IEEE Transactions on Systems, Man and Cybernetics
An algorithm for drawing general undirected graphs
Information Processing Letters
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Cell placement by self-organisation
Neural Networks
Combinatorial algorithms for integrated circuit layout
Combinatorial algorithms for integrated circuit layout
Graph drawing by force-directed placement
Software—Practice & Experience
Algorithms for drawing graphs: an annotated bibliography
Computational Geometry: Theory and Applications
Drawing graphs nicely using simulated annealing
ACM Transactions on Graphics (TOG)
Aesthetics-based graph layout for human consumption
Software—Practice & Experience
Self-organizing maps for drawing large graphs
Information Processing Letters
Visualizing Abstract Objects and Relations
Visualizing Abstract Objects and Relations
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
An Experimental Comparison of Force-Directed and Randomized Graph Drawing Algorithms
GD '95 Proceedings of the Symposium on Graph Drawing
A Fast Adaptive Layout Algorithm for Undirected Graphs
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
Mapping and hierarchical self-organizing neural networks for VLSI placement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
Structured representations in a content based image retrieval context
Journal of Visual Communication and Image Representation
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Self-organizing maps (SOM) are unsupervised, competitive neural networks used to project high-dimensional data onto a low-dimensional space. In this paper it is shown that SOM can be used to perform multidimensional scaling (MDS) on graphs. The SOM-based approach is applied to two families of random graphs and three real-world networks.