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Open Access

DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization

Nahla Mohamed Ahmed, Ling Chen( ), Yulong Wang, Bin Li, Yun Li, Wei Liu
College of Information Engineering, Yangzhou University, Yangzhou 225009, China.
College of Mathematical Sciences, Khartoum University, Khartoum, Sudan.
State Key Lab of Novel Software Tech, Nanjing University, Nanjing 210093, China.
College of Agriculture, Yangzhou University, Yangzhou 225009, China.
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Abstract

A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation, thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms.

Keywords

dynamic networklink predictionmatrix factorization

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Big Data Mining and Analytics
Volume 1 Issue 1,
March 2018
Pages 19-33
DOI: 10.26599/BDMA.2017.9020002
Cite this article:
Ahmed NM, Chen L, Wang Y, et al. DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization. Big Data Mining and Analytics, 2018, 1(1): 19-33. https://doi.org/10.26599/BDMA.2017.9020002

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Received: 27 July 2017
Accepted: 28 November 2017
Published: 25 January 2018
© The author(s) 2018
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