Smart Graph Clustering: Organizing Networks Automatically

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Table of Links

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

4.2. Properties

This part shows some general properties of the new formulation and theoretically demonstrates the inherent connection between structural entropy and graph clustering. We first give an arithmetic property regarding Definition 4.3 to support the following claim on graph clustering. The proofs of the lemma/theorems are detailed in Appendix A.





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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.

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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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