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Link prediction in evolving heterogeneous networks using the NARX neural networks

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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.

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Notes

  1. https://aminer.org.

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

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  1. Alper Ozcan & Sule Gunduz Oguducu

    Present address: Department of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, Turkey

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    1. Alper Ozcan
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    2. Sule Gunduz Oguducu
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    Correspondence to Alper Ozcan.

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    Ozcan, A., Oguducu, S.G. Link prediction in evolving heterogeneous networks using the NARX neural networks. Knowl Inf Syst 55, 333–360 (2018). https://doi.org/10.1007/s10115-017-1073-x

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