---
title: "A Survey of Structural Entropy: Theory, Methods, and Applications"
authors:
  - "Dingli Su"
  - "Hao Peng"
  - "Yicheng Pan"
  - "Angsheng Li"
doi: "10.24963/ijcai.2025/1183"
url: "https://ijcai.org/proceedings/2025/1183"
pdf: "https://ijcai.org/proceedings/2025/1183.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10660-10668"
track: "Survey Track"
keywords:
  - "Data Mining: General"
  - "Multidisciplinary Topics and Applications: General"
  - "Data Mining: DM: Mining graphs"
  - "Machine Learning: ML: Clustering"
---

# A Survey of Structural Entropy: Theory, Methods, and Applications

Dingli Su, Hao Peng, Yicheng Pan, Angsheng Li

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10660-10668. DOI: [10.24963/ijcai.2025/1183](https://doi.org/10.24963/ijcai.2025/1183)

## Abstract

Classical information theory, a cornerstone of artificial intelligence, is fundamentally limited by its local perspective, often analyzing pairwise interactions while ignoring the larger, hierarchical architecture of complex systems. Structural entropy (SE) presents a paradigm shift, extending Shannon entropy to quantify information on a global scale and measure the uncertainty embedded in a system's organizational hierarchy. Although its applications have broadened significantly from its origins in community detection across diverse AI domains, a systematic synthesis of its theory, computational methods, and applications is currently lacking.
This survey provides a comprehensive overview of SE to fill this critical void in the literature. We offer a detailed examination of its theoretical foundations, computational frameworks, and key learning paradigms, with a focus on its integration with graph learning and reinforcement learning. Through an exploration of its diverse applications, we highlight the power of SE to advance graph-based analysis and modeling. Finally, we discuss key challenges and future research opportunities for incorporating SE principles into the development of more interpretable and theoretically grounded AI systems.