{"type":"ScholarlyArticle","title":"Exploring the Over-smoothing Problem of Graph Neural Networks for Graph Classification: An Entropy-based Viewpoint","authors":[{"name":"Feifei Qian","slug":"feifei-qian","given":"Feifei","family":"Qian","affiliation":""},{"name":"Lu Bai","slug":"lu-bai","given":"Lu","family":"Bai","affiliation":""},{"name":"Lixin Cui","slug":"lixin-cui","given":"Lixin","family":"Cui","affiliation":""},{"name":"Ming Li","slug":"ming-li","given":"Ming","family":"Li","affiliation":""},{"name":"Hangyuan Du","slug":"hangyuan-du","given":"Hangyuan","family":"Du","affiliation":""},{"name":"Yue Wang","slug":"yue-wang","given":"Yue","family":"Wang","affiliation":""},{"name":"Edwin Hancock","slug":"edwin-hancock","given":"Edwin","family":"Hancock","affiliation":""}],"abstract":"The over-smoothing has emerged as a major challenge in the development of Graph Neural Networks (GNNs). While existing state-of-the-art methods effectively mitigate the diminishing distance between nodes and improve the performance of node classification, they tend to be elusive for graph-level tasks. This paper introduces a novel entropy-based perspective to explore the over-smoothing problem, simultaneously enhancing the distinguishability of non-isomorphic graphs. We provide a theoretical analysis of the relationship between the smoothness and the entropy for graphs, highlighting how the over-smoothing in high-entropic regions negatively impact the graph classification performance. To tackle this issue, we propose a simple yet effective method to Sample and Discretize node features in high-Entropic regions (SDE), aiming to preserve the critical and complicated structural information. Moreover, we introduce a new evaluation metric to assess the over-smoothing for graph-level tasks, focusing on node distributions. Experimental results demonstrate that the proposed SDE method significantly outperforms existing state-of-the-art methods, establishing a new benchmark in the field of GNNs.","doi":"10.24963/ijcai.2025/360","url":"https://ijcai.org/proceedings/2025/360","pdf":"https://ijcai.org/proceedings/2025/0360.pdf","markdown":"https://ijcai.org/proceedings/2025/360.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/360","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":"Data Mining","track":"Main Track","number":360,"pages":{"first":3235,"last":3244},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Classification","Machine Learning: ML: Deep learning architectures"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}