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Link Prediction of Complex Networks Based on Local Path and Closeness Centrality

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Computational Data and Social Networks (CSoNet 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13831))

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Abstract

Due to evolving nature of complex network, link prediction plays a crucial role in exploring likelihood of new relationships among nodes. There exist a great number of techniques to apply the similarity-based metrics for estimating proximity of vertices in the network. In this work, a novel similarity-based metric based on local path and closeness centrality is proposed, and the Local Path and Centrality based Parameterized Algorithm (LPCPA) is suggested. The proposed method is a new variant of the well-known index of Common Neighbor and Centrality based Parameterized Algorithm (CCPA). Extensive experiments are conducted on thirteen real networks originating from diverse domains. The experimental results indicate that the proposed index improves the prediction accuracy measured by AUC and has achieved a competitive result on Precision compared to the existing state-of-the-art link prediction methods.

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).

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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).

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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. College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350117, People’s Republic of China

    Shuming Zhou & Gaolin Chen

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

    Min Li

Authors
  1. Min Li
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  2. Shuming Zhou
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  3. Gaolin Chen
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Correspondence to Shuming Zhou .

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Editors and Affiliations

  1. Virginia Commonwealth University, Richmond, VA, USA

    Thang N. Dinh

  2. Yeung Kin Man Academic Building, City University Hong Kong, Kowloon Tong, Hong Kong

    Minming Li

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Li, M., Zhou, S., Chen, G. (2023). Link Prediction of Complex Networks Based on Local Path and Closeness Centrality. In: Dinh, T.N., Li, M. (eds) Computational Data and Social Networks . CSoNet 2022. Lecture Notes in Computer Science, vol 13831. Springer, Cham. https://doi.org/10.1007/978-3-031-26303-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-26303-3_5

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  • Online ISBN: 978-3-031-26303-3

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