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A survey on network node ranking algorithms: Representative methods, extensions, and applications

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Abstract

The ranking of network node importance is one of the most essential problems in the field of network science. Node ranking algorithms serve as an essential part in many application scenarios such as search engine, social networks, and recommendation systems. This paper presents a systematic review on three representative methods: node ranking based on centralities, PageRank algorithm, and HITS algorithm. Furthermore, we investigate the latest extensions and improvements of these representative methods, provided with several main application fields. Inspired by the survey of current literatures, we attempt to propose promising directions for future research. The conclusions of this paper are enlightening and beneficial to both the academic and industrial communities.

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Authors and Affiliations

  1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    JiaQi Liu & JiChang Dong

  2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    XueRong Li

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  1. JiaQi Liu
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  2. XueRong Li
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  3. JiChang Dong
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Correspondence to XueRong Li.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No. 71901205).

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Liu, J., Li, X. & Dong, J. A survey on network node ranking algorithms: Representative methods, extensions, and applications. Sci. China Technol. Sci. 64, 451–461 (2021). https://doi.org/10.1007/s11431-020-1683-2

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