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

Written by hyperbole | Published 2026/02/14
Tech Story Tags: deep-learning | deep-graph-clustering | structural-information-theory | lorentz-hyperbolic-space | lsenet-neural-network | dsi | manifold | graph-self-organization

TLDRStop 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.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

4.3. Differentiability & Deep Graph Clustering

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.


Written by hyperbole | Amplifying words and ideas to separate the ordinary from the extraordinary, making the mundane majestic.
Published by HackerNoon on 2026/02/14