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Exploiting Non-visible Relationship in Link Prediction Based on Asymmetric Local Random Walk

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Link prediction is an important aspect of complex network evolution analysis. In the existing link prediction algorithms, the sparseness and scale of the target network have a great influence on the prediction results, and the link prediction algorithm based on local random walk is better in solving this problem. However, the existing local random walk link prediction algorithm simplifiy the definition of random walk process between nodes as symmetrical relationship, and ignore the influence of non-visible factors on the relationship of information diffusion between nodes. In this paper, for the first time, we introduce asymmetry and non-visible relationship of the network to the link prediction problem. Exploiting the unequal diffusion weights in different directions resulted from different degrees, we propose an asymmetric local random walk (ALRW) algorithm. In addition, with non-visible relationship to calculate of the similarity index, we propose a grounded asymmetric local random walk (GALRW) algorithm on the basis of ALRW. Compared with existing advanced link prediction algorithms, thorough experiments on typical datasets show that GALRW achieves better performance in prediction accuracy.

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Authors and Affiliations

  1. College of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China

    Chunlong Fan, Dong Li, Yiping Teng, Dongwan Fan & Guohui Ding

Authors
  1. Chunlong Fan
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  2. Dong Li
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  3. Yiping Teng
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  4. Dongwan Fan
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  5. Guohui Ding
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Corresponding author

Correspondence to Dong Li .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Fan, C., Li, D., Teng, Y., Fan, D., Ding, G. (2017). Exploiting Non-visible Relationship in Link Prediction Based on Asymmetric Local Random Walk. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_74

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_74

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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