MLLMs given the same instructions as human participants achieve expert-level performance on perceiving stress in network visualizations and rely on similar visual proxies.
X., Pawar S., Goodman D
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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.
citing papers explorer
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Exploring MLLMs Perception of Network Visualization Principles
MLLMs given the same instructions as human participants achieve expert-level performance on perceiving stress in network visualizations and rely on similar visual proxies.
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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.