{"type":"ScholarlyArticle","title":"Heterophily-Aware Personalized PageRank for Node Classification","authors":[{"name":"Giuseppe Pirrò","slug":"giuseppe-pirro","given":"Giuseppe","family":"Pirrò","affiliation":""}],"abstract":"Node classification in heterophilous graphs, where connected nodes often have different characteristics, which presents a significant challenge. We introduce HAPPY, which combines heterophily-aware random walks with targeted subgraph extraction. Our approach enhances Personalized PageRank by incorporating both label and feature diversity into the random walk process. Through theoretical analysis, we demonstrate that HAPPY effectively captures both homophilous and heterophilous relationships. Comprehensive experiments validate our method’s state-of-the-art performance across challenging heterophilous benchmarks.","doi":"10.24963/ijcai.2025/676","url":"https://ijcai.org/proceedings/2025/676","pdf":"https://ijcai.org/proceedings/2025/0676.pdf","markdown":"https://ijcai.org/proceedings/2025/676.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/676","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":"Machine Learning","track":"Main Track","number":676,"pages":{"first":6075,"last":6083},"keywords":["Machine Learning: ML: Representation learning","Machine Learning: ML: Deep learning architectures","Multidisciplinary Topics and Applications: MTA: Web and social networks","Machine Learning: ML: Geometric learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}