1. A Fusion Link Prediction Method Based on Limit Theorem
- Author
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Ruiyang Huang, Yiteng Wu, Hongtao Yu, Yingle Li, and Senjie Lin
- Subjects
Computer science ,theoretical limit ,Probability density function ,Field (mathematics) ,02 engineering and technology ,lcsh:Technology ,01 natural sciences ,lcsh:Chemistry ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,Limit (mathematics) ,information_technology_data_management ,010306 general physics ,Link (knot theory) ,lcsh:QH301-705.5 ,Instrumentation ,link prediction ,Mathematics ,Fluid Flow and Transfer Processes ,Discrete mathematics ,Fusion ,lcsh:T ,Process Chemistry and Technology ,General Engineering ,Similarity matrix ,Object (computer science) ,combination method ,lcsh:QC1-999 ,Computer Science Applications ,lcsh:Biology (General) ,lcsh:QD1-999 ,lcsh:TA1-2040 ,TLF method ,020201 artificial intelligence & image processing ,lcsh:Engineering (General). Civil engineering (General) ,Combination method ,Algorithm ,lcsh:Physics - 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.
- Published
- 2017
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