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Link prediction based on local information considering preferential attachment

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  • Zeng, Shan

Abstract

Link prediction in complex networks has attracted much attention in many fields. In this paper, a common neighbors plus preferential attachment index is presented to estimate the likelihood of the existence of a link between two nodes based on local information of the nearest neighbors. Numerical experiments on six real networks demonstrated the high effectiveness and efficiency of the new index compared with five well-known and widely accepted indices: the common neighbors, resource allocation index, preferential attachment index, local path index and Katz index. The new index provides competitively accurate prediction with local path index and Katz index while has less computational complexity and is more accurate than the other two indices.

Suggested Citation

  • Zeng, Shan, 2016. "Link prediction based on local information considering preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 537-542.
    • Handle: RePEc:eee:phsmap:v:443:y:2016:i:c:p:537-542
    DOI: 10.1016/j.physa.2015.10.016
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    References listed on IDEAS

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    1. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    2. Xie, Yan-Bo & Zhou, Tao & Wang, Bing-Hong, 2008. "Scale-free networks without growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(7), pages 1683-1688.
    3. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    4. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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    Cited by:

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    5. Wei Chen & Hui Qu & Kuo Chi, 2021. "Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches," Sustainability, MDPI, vol. 13(2), pages 1-16, January.

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