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Research on Link Prediction Algorithms Based on Multichannel Structure Modelling

  • Conference paper
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Data Science (ICPCSEE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1880))

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Abstract

Today's link prediction methods are based on the network structure using a single-channel approach for prediction, and there is a lack of link prediction algorithms constructed from a multichannel approach, which makes the features monotonous and noncomplementary. To address this problem, this paper proposes a link prediction algorithm based on multichannel structure modelling (MCLP). First, the network is sampled three times to construct its three subgraph structures. Second, the node representation vectors of the network are learned separately for each subgraph on a single channel. Then, the three node representation vectors are combined, and the similarity matrix is calculated for the combined vectors. Finally, the performance of the MCLP algorithm is evaluated by calculating the AUC using the similarity matrix and conducting multiple experiments on three citation network datasets. The experimental results show that the proposed link prediction algorithm has an AUC of 98.92%, which is better than the performance of the 24 link prediction comparison algorithms used in this paper. The experimental results sufficiently prove that the MCLP algorithm can effectively extract the relationships between network nodes, and confirm its effectiveness and feasibility.

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Acknowledgement

This article was supported by the National Key Research and Development Program of China (No. 2020YFC1523300) and the Innovation Platform Construction Project of Qinghai Province (2022-ZJ-T02).

Author information

Authors and Affiliations

  1. College of Computer, Qinghai Normal University, Xining, Qinghai, China

    Gege Li, Lin Zhou, Zhonglin Ye & Haixing Zhao

  2. The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining, Qinghai, China

    Gege Li, Lin Zhou, Zhonglin Ye & Haixing Zhao

  3. Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province, Xining, Qinghai, China

    Gege Li, Lin Zhou, Zhonglin Ye & Haixing Zhao

  4. Key Laboratory of Tibetan Information Processing, Ministry of Education, Xining, Qinghai, China

    Gege Li, Lin Zhou, Zhonglin Ye & Haixing Zhao

Authors
  1. Gege Li
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  2. Lin Zhou
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  3. Zhonglin Ye
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  4. Haixing Zhao
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Corresponding author

Correspondence to Zhonglin Ye .

Editor information

Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Zhiwen Yu

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Hongzhi Wang

  4. Northwestern Polytechnical University, Xi'an, China

    Bin Guo

  5. Shiga University, Shiga, Japan

    Xiaokang Zhou

  6. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  7. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, G., Zhou, L., Ye, Z., Zhao, H. (2023). Research on Link Prediction Algorithms Based on Multichannel Structure Modelling. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1880. Springer, Singapore. https://doi.org/10.1007/978-981-99-5971-6_20

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  • DOI: https://doi.org/10.1007/978-981-99-5971-6_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5970-9

  • Online ISBN: 978-981-99-5971-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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