Link prediction in complex networks: a mutual information perspective.

Fei Tan,Boyao Zhu,Yongxiang Xia,Wang Zhan

doi:10.1371/journal.pone.0107056

Fei Tan, Boyao Zhu + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0107056

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Journal: PloS one	Publication Date: Sep 10, 2014
Citations: 166	License type: CC BY 4.0

Affiliation: Zhejiang University

Abstract

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

Highlights

Link prediction attempts to estimate the likelihood of the existence of links between nodes based on the available network information, such as the observed links and nodes’ attributes [1,2]
Each evolving model can be viewed as the corresponding predictor, we can apply evaluating metrics on prediction accuracy to measure the performance of different models
The lack of common neighbors between two nodes often appear in real-world networks

Summary

Introduction

Link prediction attempts to estimate the likelihood of the existence of links between nodes based on the available network information, such as the observed links and nodes’ attributes [1,2]. The link-prediction problem is a long-standing practical scientific issue. It can find broad applications in both identifying missing and spurious links and predicting the candidate links that are expected to appear with the evolution of networks [1,3,4]. In online social networks, very promising candidate links (non-connected node pairs) can be recommended to the relevant users as potential friendships [8,9]. [9], the authors even proposed the potential theory to facilitate the missing link prediction of directed networks. It is very hard to quantify the degree to which the proposed evolving models govern real networks. Each evolving model can be viewed as the corresponding predictor, we can apply evaluating metrics on prediction accuracy to measure the performance of different models

Methods

Results

Conclusion