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In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted\/unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in different test cases. <\/jats:p>","DOI":"10.1142\/s0219622018500530","type":"journal-article","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T03:18:31Z","timestamp":1538709511000},"page":"241-286","source":"Crossref","is-referenced-by-count":35,"title":["Multivariate Time Series Link Prediction for Evolving Heterogeneous Network"],"prefix":"10.1142","volume":"18","author":[{"given":"Alper","family":"Ozcan","sequence":"first","affiliation":[{"name":"Department of Computer and Informatics Engineering, Istanbul Technical University, Maslak 34357, Istanbul, Turkey"}]},{"given":"Sule Gunduz","family":"Oguducu","sequence":"additional","affiliation":[{"name":"Department of Computer and Informatics Engineering, Istanbul Technical University, Maslak 34357, Istanbul, 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