{"type":"ScholarlyArticle","title":"TensorCast: Forecasting Time-Evolving Networks with Contextual Information","authors":[{"name":"Miguel Araújo","slug":"miguel-araujo","given":"Miguel","family":"Araújo","affiliation":"University of Porto and CRACS/INESC-TEC; School of Computer Science , Carnegie Mellon University"},{"name":"Pedro Ribeiro","slug":"pedro-ribeiro","given":"Pedro","family":"Ribeiro","affiliation":"University of Porto and CRACS/INESC-TEC"},{"name":"Christos Faloutsos","slug":"christos-faloutsos","given":"Christos","family":"Faloutsos","affiliation":"School of Computer Science , Carnegie Mellon University"}],"abstract":"Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TensorCast, an award winning method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TensorCast is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.","doi":"10.24963/ijcai.2018/721","url":"https://ijcai.org/proceedings/2018/721","pdf":"https://ijcai.org/proceedings/2018/0721.pdf","markdown":"https://ijcai.org/proceedings/2018/721.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/721","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":"Sister Conferences Best Papers","track":"Best Sister Conferences","number":721,"pages":{"first":5199,"last":5203},"keywords":["Machine Learning: Time-series;Data Streams","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Araujo et al., 2014] Miguel Araujo, Stephan Günnemann, Gonzalo Mateos, and Christos Faloutsos. 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