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Smart Data Grouping: LSEnet & Automated Graph Clustering in Curved Space

Written by @hyperbole | Published on 2026/2/14
TL;DR —
Stop guessing your cluster numbers. Discover LSEnet, a deep graph clustering model that uses Differentiable Structural Information (DSI) and the Lorentz model of hyperbolic space to organize complex networks automatically.

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

4.3. Differentiability & Deep Graph Clustering

Authors:

(1) Li Sun, North China Electric Power University, Beijing 102206, China (ccesunli@ncepu.edu);

(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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Written by
@hyperbole
Amplifying words and ideas to separate the ordinary from the extraordinary, making the mundane majestic.
Topics and
tags
deep-learning|deep-graph-clustering|structural-information-theory|lorentz-hyperbolic-space|lsenet-neural-network|dsi|manifold|graph-self-organization
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