Automatic Data Grouping: LSEnet and the Future of Self-Organizing Networks
18 Feb 2026
By minimizing structural entropy in hyperbolic space, this model uncovers natural data groups automatically for more accurate network analysis.
Better Results in Curved Space: Tuning Tree Height and Embedding Power
18 Feb 2026
Learn about the superior expressiveness of hyperbolic embeddings for link prediction compared to traditional Euclidean models.
Automatic Data Sorting: How LSEnet Wins Without Guessing Numbers
18 Feb 2026
See how it uses curved space and structural entropy to find natural data groups automatically—no cluster numbers required.
Self-Organizing Networks: Training Hyperbolic Partitioning Trees with LSEnet
18 Feb 2026
Learn how the Hyperbolic Partitioning Tree combines structural entropy and curved space to automate graph clustering
Flexible Hierarchy Learning: Managing Unknown Node Counts in Curved Space
18 Feb 2026
Discover how using redundant nodes and hyperbolic gyro-midpoints preserves structural entropy while uncovering a graph's natural self-organization.
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