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Discovering Correlation Indices for Link Prediction Using Differential Evolution

Author

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  • Giulio Biondi

    (Department of Mathematics and Computer Science, University of Florence, 50121 Florence, Italy
    These authors contributed equally to this work.)

  • Valentina Franzoni

    (Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
    These authors contributed equally to this work.)

Abstract

Binary correlation indices are crucial for forecasting and modelling tasks in different areas of scientific research. The setting of sound binary correlations and similarity measures is a long and mostly empirical interactive process, in which researchers start from experimental correlations in one domain, which usually prove to be effective in other similar fields, and then progressively evaluate and modify those correlations to adapt their predictive power to the specific characteristics of the domain under examination. In the research of prediction of links on complex networks, it has been found that no single correlation index can always obtain excellent results, even in similar domains. The research of domain-specific correlation indices or the adaptation of known ones is therefore a problem of critical concern. This paper presents a solution to the problem of setting new binary correlation indices that achieve efficient performances on specific network domains. The proposed solution is based on Differential Evolution, evolving the coefficient vectors of meta-correlations, structures that describe classes of binary similarity indices and subsume the most known correlation indices for link prediction. Experiments show that the proposed evolutionary approach always results in improved performances, and in some cases significantly enhanced, compared to the best correlation indices available in the link prediction literature, effectively exploring the correlation space and exploiting its self-adaptability to the given domain to improve over generations.

Suggested Citation

  • Giulio Biondi & Valentina Franzoni, 2020. "Discovering Correlation Indices for Link Prediction Using Differential Evolution," Mathematics, MDPI, vol. 8(11), pages 1-10, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2097-:d:449804
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
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    2. Ibrahim Attiya & Laith Abualigah & Doaa Elsadek & Samia Allaoua Chelloug & Mohamed Abd Elaziz, 2022. "An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing," Mathematics, MDPI, vol. 10(7), pages 1-18, March.

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