Symmetry-driven embedding of networks in hyperbolic space

arXiv:2406.10711 · stat.CO, cs.SI, stat.ML · Submitted 2024-06-15 · Read on arXiv

stat.CO, cs.SI, stat.ML

Submitted: 2024-06-15

Updated: 2025-04-24

DOI: 10.1038/s42005-025-02122-0

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 40× in this paper · explained in 2 episodes
• Clustering — 15× in this paper · explained in 2 episodes
• Hamiltonian Monte Carlo — 4× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Bayesian Framework — 2× in this paper · explained in 3 episodes
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Modularity — 2× in this paper · explained in 9 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Degeneracy — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes

Sources

• Neural Embeddings of Graphs in Hyperbolic Space
• Latent Space Network Modelling with Hyperbolic and Spherical Geometries
• Adaptive Path Sampling in Metastable Posterior Distributions
• Sampling from high-dimensional, multimodal distributions using automatically tuned, tempered Hamiltonian Monte Carlo
• Continuously tempered Hamiltonian Monte Carlo
• A Conceptual Introduction to Hamiltonian Monte Carlo
• Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings

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