Version 1
: Received: 26 October 2017 / Approved: 26 October 2017 / Online: 26 October 2017 (05:49:34 CEST)
How to cite:
Wu, Y.; Yu, H.; Huang, R.; Li, Y.; Lin, S. A Fusion Link Prediction Method Based on Limit Theorem. Preprints2017, 2017100163. https://doi.org/10.20944/preprints201710.0163.v1
Wu, Y.; Yu, H.; Huang, R.; Li, Y.; Lin, S. A Fusion Link Prediction Method Based on Limit Theorem. Preprints 2017, 2017100163. https://doi.org/10.20944/preprints201710.0163.v1
Wu, Y.; Yu, H.; Huang, R.; Li, Y.; Lin, S. A Fusion Link Prediction Method Based on Limit Theorem. Preprints2017, 2017100163. https://doi.org/10.20944/preprints201710.0163.v1
APA Style
Wu, Y., Yu, H., Huang, R., Li, Y., & Lin, S. (2017). A Fusion Link Prediction Method Based on Limit Theorem. Preprints. https://doi.org/10.20944/preprints201710.0163.v1
Chicago/Turabian Style
Wu, Y., Yingle Li and Senjie Lin. 2017 "A Fusion Link Prediction Method Based on Limit Theorem" Preprints. https://doi.org/10.20944/preprints201710.0163.v1
Abstract
The theoretical limit of link prediction is a fundamental problem in this field. Taking the network structure as object to research this problem is the mainstream method. This paper proposes a new viewpoint that link prediction methods can be divided into single or combination methods, based on the way they derive the similarity matrix, and investigates whether there a theoretical limit exists for combination methods. We propose and prove necessary and sufficient conditions for the combination method to reach the theoretical limit. The limit theorem reveals the essence of combination method that is to estimate probability density functions of existing links and nonexistent links. Based on limit theorem, a new combination method, theoretical limit fusion (TLF) method, is proposed. Simulations and experiments on real networks demonstrated that TLF method can achieve higher prediction accuracy.
Keywords
link prediction; combination method; theoretical limit; TLF method
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
