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Missing link prediction using path and community information

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Abstract

Due to the evolving nature of complex networks, link prediction plays a crucial role in exploring likelihood of potential relationships among nodes. There exist a great number of strategies to apply the similarity-based metrics for estimating proximity of nodes in complex networks. In this paper, we propose three new variants – CCPAL3, LPCPA, and GPCPA – for the well-known Common Neighbor and Centrality-based Parameterized Algorithm (CCPA) taking into account 3-hop path, quasi-local path, and global path, respectively. In addition, four novel link prediction strategies based on community detection information, CCPA_CD, CCPAL3_CD, LPCPA_CD and GPCPA_CD, are proposed. Meanwhile, the Jaccard index is extended to three new metrics, i.e., Jaccard_L3, Jaccard_QuasiLoc and Jaccard_Global. Extensive experiments are conducted on thirteen real-world networks. The experimental results indicate that the proposed metrics improve the prediction accuracy measured by AUC and are more competitive on Precision compared to the state-of-the-art link prediction methods.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Nos. 61977016 and 61572010), Natural Science Foundation of Fujian Province (Nos. 2020J01164, 2017J01738) and Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province (No. JAT191119). This work was also partly supported by China Scholarship Council (CSC No. 202108350054).

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Authors and Affiliations

  1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, 350117, People’s Republic of China

    Min Li & Gaolin Chen

  2. Concord University College, Fujian Normal University, Fuzhou, Fujian, 350117, People’s Republic of China

    Min Li

  3. School of Mathematics and Statistics, Key Laboratory of Analytical Mathematics and Applications (Ministry of Education), Center for Applied Mathematics of Fujian Province, Fujian Normal University, Fuzhou, Fujian, 350117, China

    Shuming Zhou

  4. Department of Computer Science, Montclair State University, Upper Montclair, 07043, NJ, USA

    Dajin Wang

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  1. Min Li
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Correspondence to Shuming Zhou.

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Li, M., Zhou, S., Wang, D. et al. Missing link prediction using path and community information. Computing 106, 521–555 (2024). https://doi.org/10.1007/s00607-023-01229-y

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