Connecting patterns inspire link prediction in complex networks
type: Item
instance of: scholarly article
title: Connecting patterns inspire link prediction in complex networks
zbMATH Open document ID: 1380.93059
DOI: 10.1155/2017/8581365
author: Ming-Yang Zhou
author: Hao Liao
author: Wen-Man Xiong
author: Xiang-Yang Wu
author: Zong-Wen Wei
published in: Complexity
publication date: 2018-01-29T00:00:00Z
review text: Summary: Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks.
Mathematics Subject Classification ID: 93A30
Mathematics Subject Classification ID: 90B15
Mathematics Subject Classification ID: 93A15
zbMATH DE Number: 6830332
zbMATH Keywords: complex networks
zbMATH Keywords: network analysis
zbMATH Keywords: potential relations
zbMATH Keywords: similarity scores of two nodes
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/8581365
OpenAlex ID: W2778891470
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description: scientific article; zbMATH DE number 6830332