{"type":"ScholarlyArticle","title":"SPINE: Structural Identity Preserved Inductive Network Embedding","authors":[{"name":"Junliang Guo","slug":"junliang-guo","given":"Junliang","family":"Guo","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Linli Xu","slug":"linli-xu","given":"Linli","family":"Xu","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Jingchang Liu","slug":"jingchang-liu","given":"Jingchang","family":"Liu","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , School of Computer Science and Technology , University of Science and Technology of China"}],"abstract":"Recent advances in the field of network embedding have shown that\r\nlow-dimensional network representation is playing\r\na critical role in network analysis. Most existing network embedding\r\nmethods encode the local proximity of a node,\r\nsuch as the first- and second-order proximities.\r\nWhile being efficient, these methods are short of leveraging the global structural information\r\nbetween nodes distant from each other.\r\nIn addition, most existing methods learn embeddings on one single fixed network,\r\nand thus cannot be generalized to unseen nodes or networks without retraining.\r\nIn this paper we present SPINE, a method that can jointly capture the local proximity and\r\nproximities at any distance, while being inductive to efficiently deal with unseen nodes or networks.\r\nExtensive experimental results on benchmark datasets\r\ndemonstrate the superiority of the proposed framework\r\nover the state of the art.","doi":"10.24963/ijcai.2019/333","url":"https://ijcai.org/proceedings/2019/333","pdf":"https://ijcai.org/proceedings/2019/0333.pdf","markdown":"https://ijcai.org/proceedings/2019/333.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/333","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":333,"pages":{"first":2399,"last":2405},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Batagelj et al., 1992] Vladimir Batagelj, Anuška Ferligoj, and Patrick Doreian. 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