Link prediction in evolving heterogeneous networks using the NARX neural networks

Alper Ozcan*, Sule Gunduz Oguducu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

In this article, we propose a novel multivariate method for link prediction in evolving heterogeneous networks using a Nonlinear Autoregressive Neural Network with External Inputs (NARX). The proposed method combines (1) correlations between different link types; (2) the effects of different topological local and global similarity measures in different time periods; (3) nonlinear temporal evolution information; (4) the effects of the creation, preservation or removal of the links between the node pairs in consecutive time periods. We evaluate the performance of link prediction in terms of different AUC measures. Experiments on real networks demonstrate that the proposed multivariate method using NARX outperforms the previous temporal methods using univariate time series in different test cases.

Original languageEnglish
Pages (from-to)333-360
Number of pages28
JournalKnowledge and Information Systems
Volume55
Issue number2
DOIs
Publication statusPublished - 1 May 2018

Bibliographical note

Publisher Copyright:
© 2017, Springer-Verlag London Ltd.

Funding

https://delicious.com/ which is collected as part of a research project (110E027) supported by Technological Research Council of Turkey (TUBITAK).

FundersFunder number
TUBITAK
Technological Research Council of Turkey

    Keywords

    • Evolving networks
    • Heterogeneous social network analysis
    • Link prediction
    • NARX
    • Node similarities

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