Lorentzian Logic: Solving Unknown Cluster Numbers via Differentiable Graph Entropy
14 Feb 2026
y leveraging the Lorentz model of hyperbolic space and differentiable structural information, it identifies optimal hierarchies through self-organized gradient
LSEnet: Mastering Automated Data Grouping in Curved Hyperbolic Space
14 Feb 2026
By ditching flat Euclidean space for curved hyperbolic geometry, it recursively learns parent nodes using a self-supervised clustering objective.
Smart Data Grouping: LSEnet & Automated Graph Clustering in Curved Space
14 Feb 2026
Discover LSEnet, a deep graph clustering model that uses Differentiable Structural Information (DSI)
Smart Graph Clustering: Organizing Networks Automatically
14 Feb 2026
Master Kuramoto model training, hyperbolic reinforcement learning, and deep graph clustering without predefined cluster numbers using LSEnet
Smart Data Grouping: Organizing Networks Without Guesswork
13 Feb 2026
Learn how to perform deep graph clustering without a predefined cluster number K using Lorentz hyperbolic models and H-dimensional structural information.
LSEnet: A Smarter Way to Organize Data Using Curved Space
13 Feb 2026
Learn how H-dimensional Structural Entropy minimizes uncertainty and enables optimization without a predefined cluster number.
Generalized Kuramoto Models & Hyperbolic Graph Clustering in Lorentz Space
13 Feb 2026
Learn about generalized Kuramoto oscillators on unit spheres and hierarchical clustering within the Lorentz model of hyperbolic space.
Riemannian Graph Learning & Structural Entropy: Deep Node Clustering in 2026
13 Feb 2026
Learn how Hyperbolic Riemannian manifolds and Lorentz models solve complex node clustering with unknown cluster numbers.
LSEnet: Deep Graph Clustering with Unknown Cluster Numbers via Lorentz Hyperbolic Space
13 Feb 2026
Discover LSEnet, a novel deep graph clustering model that solves the problem of unknown cluster numbers using Differentiable Structural Information (DSI)