{"type":"ScholarlyArticle","title":"Lightweight Label Propagation for Large-Scale Network Data","authors":[{"name":"De-Ming Liang","slug":"de-ming-liang","given":"De-Ming","family":"Liang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023 , China"},{"name":"Yu-Feng Li","slug":"yu-feng-li","given":"Yu-Feng","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023 , China"}],"abstract":"Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \\algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm can handle million-scale graphs in few seconds while achieving highly competitive performance with existing algorithms.","doi":"10.24963/ijcai.2018/475","url":"https://ijcai.org/proceedings/2018/475","pdf":"https://ijcai.org/proceedings/2018/0475.pdf","markdown":"https://ijcai.org/proceedings/2018/475.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/475","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":475,"pages":{"first":3421,"last":3427},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning Applications: Big data ; Scalability"],"videos":[],"references":[{"position":1,"raw":"[Bottou, 2010] Léon Bottou. 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