{"type":"ScholarlyArticle","title":"Link Prediction with Spatial and Temporal Consistency in Dynamic Networks","authors":[{"name":"Wenchao Yu","slug":"wenchao-yu","given":"Wenchao","family":"Yu","affiliation":"Department of Computer Science , University of California Los Angeles"},{"name":"Wei Cheng","slug":"wei-cheng","given":"Wei","family":"Cheng","affiliation":"NEC Laboratories America , Inc."},{"name":"Charu C Aggarwal","slug":"charu-c-aggarwal","given":"Charu C","family":"Aggarwal","affiliation":"IBM T.J. Watson Research Center"},{"name":"Haifeng Chen","slug":"haifeng-chen","given":"Haifeng","family":"Chen","affiliation":"NEC Laboratories America , Inc."},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"Department of Computer Science , University of California Los Angeles"}],"abstract":"Dynamic networks are ubiquitous. Link prediction in dynamic networks has attracted tremendous research interests. Many models have been developed to predict links that may emerge in the immediate future from the past evolution of the networks.  There are two key factors: 1) a node is more likely to form a link in the near future with another node within its close proximity, rather than with a random node; 2)  a dynamic network usually evolves smoothly. Existing approaches seldom unify these two factors to strive for the spatial and temporal consistency in a dynamic network. To address this limitation, in this paper, we propose a link prediction model with spatial and temporal consistency (LIST), to predict links in a sequence of networks over time. LIST characterizes the  network dynamics as a function of time, which integrates the spatial topology of network at each timestamp and the temporal network evolution. Comparing to existing approaches, LIST has two advantages: 1) LIST uses a generic model to express the network structure as a function of time, which makes it also suitable for  a wide variety of temporal network analysis problems beyond the focus of this paper; 2) by retaining the spatial and temporal consistency, LIST yields better prediction performance. Extensive experiments on four real datasets demonstrate the effectiveness of the LIST model.","doi":"10.24963/ijcai.2017/467","url":"https://ijcai.org/proceedings/2017/467","pdf":"https://ijcai.org/proceedings/2017/0467.pdf","markdown":"https://ijcai.org/proceedings/2017/467.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/467","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":467,"pages":{"first":3343,"last":3349},"keywords":["Machine Learning: Time-series/Data Streams","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Subbian, 2014] Charu Aggarwal and Karthik Subbian. 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