{"type":"ScholarlyArticle","title":"Learning Label Initialization for Time-Dependent Harmonic Extension.","authors":[{"name":"Amitoz Azad","slug":"amitoz-azad","given":"Amitoz","family":"Azad","affiliation":"University of Caen Normandy"}],"abstract":"Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version of the Dirichlet problem on graphs and shows how to improve its solution by learning the proper initialization vector on the unlabeled nodes. Further, we show that the improved solution is at par with state-of-the-art methods used for node classification. Finally, we conclude this paper by discussing the importance of parameter t, pros, and future directions.","doi":"10.24963/ijcai.2022/387","url":"https://ijcai.org/proceedings/2022/387","pdf":"https://ijcai.org/proceedings/2022/0387.pdf","markdown":"https://ijcai.org/proceedings/2022/387.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/387","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":387,"pages":{"first":2791,"last":2797},"keywords":["AI Ethics, Trust, Fairness: Trustworthy AI","Machine Learning: Representation learning","Machine Learning: Semi-Supervised Learning","Machine Learning: Other"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986981","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985124","length_sec":667,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Belkin et al., 2004] Mikhail Belkin, Irina Matveeva, and Partha Niyogi. 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