Link prediction in evolving heterogeneous networks using the NARX neural networks

Ozcan A., Öğüdücü Ş.

KNOWLEDGE AND INFORMATION SYSTEMS, cilt.55, sa.2, ss.333-360, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s10115-017-1073-x
  • Dergi Adı: KNOWLEDGE AND INFORMATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.333-360
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.