An information-theoretic model for link prediction in complex networks

Scientific Reports
Boyao Zhu, Yongxiang Xia

Abstract

Various structural features of networks have been applied to develop link prediction methods. However, because different features highlight different aspects of network structural properties, it is very difficult to benefit from all of the features that might be available. In this paper, we investigate the role of network topology in predicting missing links from the perspective of information theory. In this way, the contributions of different structural features to link prediction are measured in terms of their values of information. Then, an information-theoretic model is proposed that is applicable to multiple structural features. Furthermore, we design a novel link prediction index, called Neighbor Set Information (NSI), based on the information-theoretic model. According to our experimental results, the NSI index performs well in real-world networks, compared with other typical proximity indices.

References

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Citations

Jan 21, 2016·PloS One·Bo OuyangZhaosheng Teng
Dec 3, 2016·Scientific Reports·Jinxuan Yang, Xiao-Dong Zhang
Mar 28, 2017·Scientific Reports·Hadi Shakibian, Nasrollah Moghadam Charkari
Apr 15, 2020·Scientific Reports·Said KerracheHafida Benhidour
Sep 8, 2018·Scientific Reports·Zhiwei CaoShuigeng Zhou
Jun 26, 2021·Scientific Reports·Seyed Hossein JafariNaser Yazdani

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Software Mentioned

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