{"type":"ScholarlyArticle","title":"Fast Change Point Detection on Dynamic Social Networks","authors":[{"name":"Yu Wang","slug":"yu-wang","given":"Yu","family":"Wang","affiliation":"* Department of Computer Science and Engineering # Department of Statistics The Ohio State University , Columbus , Ohio , USA"},{"name":"Aniket Chakrabarti","slug":"aniket-chakrabarti","given":"Aniket","family":"Chakrabarti","affiliation":"* Department of Computer Science and Engineering # Department of Statistics The Ohio State University , Columbus , Ohio , USA"},{"name":"David Sivakoff","slug":"david-sivakoff","given":"David","family":"Sivakoff","affiliation":"* Department of Computer Science and Engineering # Department of Statistics The Ohio State University , Columbus , Ohio , USA"},{"name":"Srinivasan Parthasarathy","slug":"srinivasan-parthasarathy","given":"Srinivasan","family":"Parthasarathy","affiliation":"* Department of Computer Science and Engineering # Department of Statistics The Ohio State University , Columbus , Ohio , USA"}],"abstract":"A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.","doi":"10.24963/ijcai.2017/417","url":"https://ijcai.org/proceedings/2017/417","pdf":"https://ijcai.org/proceedings/2017/0417.pdf","markdown":"https://ijcai.org/proceedings/2017/417.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/417","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":417,"pages":{"first":2992,"last":2998},"keywords":["Machine Learning: Time-series/Data Streams","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2014] Leman Akoglu, Hanghang Tong, and Danai Koutra. 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