Presents a scalable randomized algorithm for geometric crossing minimization, including a theoretical approximation guarantee for vertex repositioning and experimental results on graphs with up to 13,000 edges.
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Adapting SGD from graph drawing produces a scikit-learn compatible stochastic solver that converges faster than SMACOF for global stress minimization while achieving comparable or lower stress on benchmarks.
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Geometric Crossing-Minimization -- A Scalable Randomized Approach
Presents a scalable randomized algorithm for geometric crossing minimization, including a theoretical approximation guarantee for vertex repositioning and experimental results on graphs with up to 13,000 edges.
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Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
Adapting SGD from graph drawing produces a scikit-learn compatible stochastic solver that converges faster than SMACOF for global stress minimization while achieving comparable or lower stress on benchmarks.