{"type":"ScholarlyArticle","title":"A Survey of Structural Entropy: Theory, Methods, and Applications","authors":[{"name":"Dingli Su","slug":"dingli-su","given":"Dingli","family":"Su","affiliation":""},{"name":"Hao Peng","slug":"hao-peng","given":"Hao","family":"Peng","affiliation":""},{"name":"Yicheng Pan","slug":"yicheng-pan","given":"Yicheng","family":"Pan","affiliation":""},{"name":"Angsheng Li","slug":"angsheng-li","given":"Angsheng","family":"Li","affiliation":""}],"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.\r\nThis 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.","doi":"10.24963/ijcai.2025/1183","url":"https://ijcai.org/proceedings/2025/1183","pdf":"https://ijcai.org/proceedings/2025/1183.pdf","markdown":"https://ijcai.org/proceedings/2025/1183.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1183","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Survey Track","track":"Survey Track","number":1183,"pages":{"first":10660,"last":10668},"keywords":["Data Mining: General","Multidisciplinary Topics and Applications: General","Data Mining: DM: Mining graphs","Machine Learning: ML: Clustering"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}