{"type":"ScholarlyArticle","title":"A Centrality-based Graph Learning Framework","authors":[{"name":"Jiajun Yu","slug":"jiajun-yu","given":"Jiajun","family":"Yu","affiliation":""},{"name":"Zhihao Wu","slug":"zhihao-wu","given":"Zhihao","family":"Wu","affiliation":""},{"name":"Jielong Lu","slug":"jielong-lu","given":"Jielong","family":"Lu","affiliation":""},{"name":"Tianyue Wang","slug":"tianyue-wang","given":"Tianyue","family":"Wang","affiliation":""},{"name":"Haishuai Wang","slug":"haishuai-wang","given":"Haishuai","family":"Wang","affiliation":""}],"abstract":"Graph Neural Networks (GNNs) have become powerful models for both node- and graph-level tasks. While node-level learning focuses on individual nodes and their local structures, graph-level learning encounters challenges in capturing the global properties of graphs. In this paper, we conduct a theoretical and experimental analysis of existing graph-level learning frameworks and find that these frameworks typically adopt a single-view perspective based solely on node degree, which limits their ability to capture comprehensive graph characteristics.\r\nTo address these issues, we propose a multi-view approach that leverages different types of centrality measures to capture diverse aspects of graph structure. We design an attention-based mechanism to adaptively integrate these multiple views, and use it as a readout function to perform weighted summation of node embeddings, termed as Adaptive Centrality Readout (ACRead). ACRead demonstrates enhanced flexibility and effectiveness when integrated with various GNN architectures, outperforming state-of-the-art readout methods, including KerRead and Set Transformer.\r\nAdditionally, this multi-view centrality approach can serve as a standalone graph-level learning framework without relying on GNNs, referred to as Adaptive Centrality-based Graph Learning (ACGL), which achieves competitive performance by effectively combining different centrality perspectives.","doi":"10.24963/ijcai.2025/399","url":"https://ijcai.org/proceedings/2025/399","pdf":"https://ijcai.org/proceedings/2025/0399.pdf","markdown":"https://ijcai.org/proceedings/2025/399.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/399","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":399,"pages":{"first":3588,"last":3596},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}