{"type":"ScholarlyArticle","title":"Graph Invariant Kernels","authors":[{"name":"Francesco Orsini","slug":"francesco-orsini","given":"Francesco","family":"Orsini","affiliation":""},{"name":"Paolo Frasconi","slug":"paolo-frasconi","given":"Paolo","family":"Frasconi","affiliation":""},{"name":"Luc De Raedt","slug":"luc-de-raedt","given":"Luc","family":"de Raedt","affiliation":""}],"abstract":"We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effectively exploit high-dimensional and continuous vertex attributes. Graphs are first decomposed into subgraphs. Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant. By changing this invariant we obtain a family of graph kernels which includes generalizations of Weisfeiler-Lehman, NSPDK, and propagation kernels. We demonstrate empirically that these kernels obtain state-of-the-art results on relational data sets.","doi":null,"url":"https://ijcai.org/proceedings/2015/528","pdf":"https://ijcai.org/proceedings/2015/0528.pdf","markdown":"https://ijcai.org/proceedings/2015/528.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/528","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":528,"pages":{"first":3756,"last":3761},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}