Results 191 to 200 of about 34,905 (300)

Do Graph Drawing Aesthetics Matter for AI? A Replication of Foundational Studies in Graph Readability

open access: yesComputer Graphics Forum, EarlyView.
Abstract Graph drawing aesthetics have traditionally been optimized for human readers, leading to well‐established principles such as reducing edge crossings, enhancing symmetry, and minimizing bends. These criteria shape layout algorithms and define what “readability” means in network visualization.
Sara Di Bartolomeo   +5 more
wiley   +1 more source

Geometry‐Aware Alignment and Comparison of Hierarchical Morse Complexes with Applications

open access: yesComputer Graphics Forum, EarlyView.
Abstract Scalar fields derived from 3D X‐ray CT scans of samples undergoing ex situ processes, such as thermal aging, chemical etching, or mechanical stress, pose unique challenges for characterizing similarities and differences across acquisitions. Typically, a sample A (source) is imaged, removed, and subjected to experimental conditions that alter ...
Aniketh Venkat   +3 more
wiley   +1 more source

Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization

open access: yesComputer Graphics Forum, EarlyView.
Abstract Network visualization has traditionally relied on heuristic metrics, such as stress, under the assumption that optimizing them leads to aesthetic and informative layouts. However, no single metric consistently produces the most effective results.
X. Li, P. Zhang, X. Wang, H. Shen, Y. Hu
wiley   +1 more source

Clusterix: A Hybrid Visualization Model for Hierarchically Clustered Networks

open access: yesComputer Graphics Forum, EarlyView.
Abstract We introduce Clusterix, a novel hybrid visualization model for representing hierarchically clustered networks, which also supports directed and weighted edges. Clusterix offers an integrated view of both the network and its full cluster hierarchy by compactly visualizing the cluster inclusion tree enriched with links of the network.
Carla Binucci   +6 more
wiley   +1 more source

Differentiable Randers‐Finsler Eikonal Solvers

open access: yesComputer Graphics Forum, EarlyView.
Abstract Fast and differentiable solvers for anisotropic and asymmetric distance fields are a key primitive in geometry processing, enabling gradient‐based optimization over metrics, drift fields, and downstream objectives that depend on geodesic distances and geodesics.
Barak Gahtan   +2 more
wiley   +1 more source

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