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An Empirical Evaluation of Algorithms for Link Prediction

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Abstract

Online social networks (OSNs) analysis has been widely used in the field of information systems (IS), thus link prediction, one of the most important core techniques of OSNs analysis, plays a vital role in the development of IS. Despite the recent development of numerous link prediction approaches, there is still a lack of comprehensive studies that measure and evaluate their performance, which hinders the rational selection and full utilization of existing prediction approaches. This study proposes a novel taxonomy of link prediction approaches based on their prediction principles. Furthermore, it selects eighteen representative approaches from various categories to perform an empirical evaluation on six real-world benchmark datasets. The features of different types of predication approaches have been analyzed based evaluation test results. The research provides researchers with improved understandings on link prediction approaches and offers insightful performance related information to practitioners for developing more effective information systems.

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Data Availability

The data sets supporting the results of this article are included within the article and its additional files.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (62062066, 61762090, 61966036 and 62276227), Yunnan Fundamental Research Projects (202201AS070015); Yunnan Key Laboratory of Intelligent Systems and Computing (202205AG070003), the Blockchain and Data Security Governance Engineering Research Center of Yunnan Provincial Department of Education, University Key Laboratory of Internet of Things Technology and Application of Yunnan Province.

Funding

a) National Natural Science Foundation of China Award Number: 61966036.

b) National Natural Science Foundation of China Award Number: 61762090.

c) National Natural Science Foundation of Ching Award Number: 62062066.

d) Yunnan Fundamental Research Projects Award Number: 202201AS070015.

Author information

Authors and Affiliations

  1. School of Information Science & Engineering, Yunnan University, Kunming, 650091, Yunnan, People’s Republic of China

    Tong Huang, Lihua Zhou, Lizhen Wang, Hongmei Chen & Guowang Du

  2. Brunel University, Uxbridge, UB8 3PH, UK

    Kevin Lü

Authors
  1. Tong Huang
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  2. Lihua Zhou
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  3. Kevin Lü
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  4. Lizhen Wang
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  5. Hongmei Chen
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  6. Guowang Du
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Contributions

Lihua Zhou: Conceptualization, Methodology, Software. Tong Huang: Data curation, Writing- Original draft preparation. Lizhen Wang & Hongmei Chen: Writing- Reviewing and Editing. Kevin Lü: Supervision. Guowan Du: Writing the algorithm code.

Corresponding author

Correspondence to Lihua Zhou.

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Appendix

Appendix

The “Ave “ in Table 7 means column average, and bold numbers represent the best results in the column.

Table 7 Link prediction performance comparisons of different approaches on all datasets
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Huang, T., Zhou, L., Lü, K. et al. An Empirical Evaluation of Algorithms for Link Prediction. Inf Syst Front (2023). https://doi.org/10.1007/s10796-023-10440-3

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