Multivariate Time Series Link Prediction for Evolving Heterogeneous Network

Alper Ozcan and Sule Gunduz Oguducu ()
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Alper Ozcan: Department of Computer and Informatics Engineering, Istanbul Technical University, Maslak 34357, Istanbul, Turkey
Sule Gunduz Oguducu: Department of Computer and Informatics Engineering, Istanbul Technical University, Maslak 34357, Istanbul, Turkey

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 01, 241-286

Abstract: Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. 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.

Keywords: Heterogeneous social network analysis; evolving networks; node similarities; link prediction; multivariate time series (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622018500530

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