Lorentzian Logic: Visualizing High-Fidelity Graph Hierarchies in Hyperbolic Space
19 Feb 2026
Discover how Differentiable Structural Information (DSI) and the Lorentz model identify optimal cluster structures without predefined group numbers (K).
Decoding Data: The Technical Secrets of LSEnet Algorithms and Benchmarks
19 Feb 2026
Dive into the technical implementation of LSEnet, including its Breadth-First Search (BFS) decoding algorithm and its performance against elite baselines
Curved Space Geometry: Embedding Trees and Finding Midpoints in Hyperbolic Models
19 Feb 2026
Discover how trees can be embedded into hyperbolic space with arbitrarily low distortion and explore the mathematical foundations of Frechet
Graph Information Theory: The Mathematical Proofs Behind LSEnet and DSI
19 Feb 2026
Explore the rigorous validation of equivalence, additivity, and graph clustering bounds that power LSEnet and self-organizing networks.
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.