{"type":"ScholarlyArticle","title":"Extrapolating Paths with Graph Neural Networks","authors":[{"name":"Jean-Baptiste Cordonnier","slug":"jean-baptiste-cordonnier","given":"Jean-Baptiste","family":"Cordonnier","affiliation":"École Polytechnique Fédérale de Lausanne"},{"name":"Andreas Loukas","slug":"andreas-loukas","given":"Andreas","family":"Loukas","affiliation":"École Polytechnique Fédérale de Lausanne"}],"abstract":"We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the interaction of an agent with a network---a driver on the transportation network, an information seeker in Wikipedia, or a client in an online shop. Our interest is sparked by the realization that, in contrast to shortest-path problems, natural paths are usually not optimal in any graph-theoretic sense, but might still follow predictable patterns. Our main contribution is a graph neural network called Gretel. Conditioned on a path prefix, this network can efficiently extrapolate path suffixes, evaluate path likelihood, and sample from the future path distribution. Our experiments with GPS traces on a road network and user-navigation paths in Wikipedia confirm that Gretel is able to adapt to graphs with very different properties, while also comparing favorably to previous solutions.","doi":"10.24963/ijcai.2019/303","url":"https://ijcai.org/proceedings/2019/303","pdf":"https://ijcai.org/proceedings/2019/0303.pdf","markdown":"https://ijcai.org/proceedings/2019/303.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/303","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":303,"pages":{"first":2187,"last":2194},"keywords":["Machine Learning: Deep Learning","Machine Learning: Structured Prediction"],"videos":[],"references":[{"position":1,"raw":"[Bastian et al., 2009] Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy. 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