Lorentzian Logic: Visualizing High-Fidelity Graph Hierarchies in Hyperbolic Space

Written by hyperbole | Published 2026/02/19
Tech Story Tags: deep-learning | lorentzian-graph-visualization | hyperbolic-partitioning-tree | cora-dataset-clustering | dsi | lsenet-neural-architecture | minkowski-inner-product | non-euclidean-deep-learning

TLDRExplore the visualized hyperbolic partitioning trees of the Cora dataset using LSEnet. Discover how Differentiable Structural Information (DSI) and the Lorentz model identify optimal cluster structures without predefined group numbers (K).via the TL;DR App

Abstract and 1. Introduction

  1. Related Work

  2. Preliminaries and Notations

  3. Differentiable Structural Information

    4.1. A New Formulation

    4.2. Properties

    4.3. Differentiability & Deep Graph Clustering

  4. LSEnet

    5.1. Embedding Leaf Nodes

    5.2. Learning Parent Nodes

    5.3. Hyperbolic Partitioning Tree

  5. Experiments

    6.1. Graph Clustering

    6.2. Discussion on Structural Entropy

  6. Conclusion, Broader Impact, and References Appendix

A. Proofs

B. Hyperbolic Space

C. Technical Details

D. Additional Results

D. Additional Results

The hyperbolic partitioning trees of Cora is visualized in Fig. 6, where different clusters are distinguished by colors.

Authors:

(1) Li Sun, North China Electric Power University, Beijing 102206, China ([email protected]);

(2) Zhenhao Huang, North China Electric Power University, Beijing 102206, China;

(3) Hao Peng, Beihang University, Beijing 100191, China;

(4) Yujie Wang, North China Electric Power University, Beijing 102206, China;

(5) Chunyang Liu, Didi Chuxing, Beijing, China;

(6) Philip S. Yu, University of Illinois at Chicago, IL, USA.


This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license.


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Published by HackerNoon on 2026/02/19