Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Optimal linear labelings and eigenvalues of graphs
Discrete Applied Mathematics
Graph drawing by force-directed placement
Software—Practice & Experience
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Drawing graphs: methods and models
Drawing graphs: methods and models
Visualization of bibliographic networks with a reshaped landscape metaphor
VISSYM '02 Proceedings of the symposium on Data Visualisation 2002
Graph Drawing by High-Dimensional Embedding
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
A tutorial on spectral clustering
Statistics and Computing
Spectral conformal parameterization
SGP '08 Proceedings of the Symposium on Geometry Processing
Dynamic spectral layout of small worlds
GD'05 Proceedings of the 13th international conference on Graph Drawing
A regularized graph layout framework for dynamic network visualization
Data Mining and Knowledge Discovery
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The spectral approach for graph visualization computes the layout of a graph using certain eigenvectors of related matrices. Two important advantages of this approach are an ability to compute optimal layouts (according to specific requirements) and a very rapid computation time. In this paper, we explore spectral visualization techniques and study their properties from different points of view. We also suggest a novel algorithm for calculating spectral layouts resulting in an extremely fast computation by optimizing the layout within a small vector space.