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International Conference on Complex Networks and their Applications

COMPLEX NETWORKS 2017: Complex Networks & Their Applications VI pp 93–104Cite as

Newton’s Gravitational Law for Link Prediction in Social Networks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

Abstract

Link prediction is an important research area in network science due to a wide range of real-world application. There are a number of link prediction methods. In the area of social networks, these methods are mostly inspired by social theory, such as having more mutual friends between two people in a social network platform entails higher probability of those two people becoming friends in the future. In this paper we take our inspiration from a different area, which is Newton’s law of universal gravitation. Although this law deals with physical bodies, based on our intuition and empirical results we found that this could also work in networks, and especially in social networks. In order to apply this law, we had to endow nodes with the notion of mass and distance. While node importance could be considered as mass, the shortest path, path count, or inverse similarity (AdamicAdar, Katz score etc.) could be considered as distance. In our analysis, we have primarily used degree centrality to denote the mass of the nodes, while the lengths of shortest paths between them have been used as distances. In this study we compare the proposed link prediction approach to 7 other methods on 4 datasets from various domains. To this end, we use the ROC curves and the AUC measure to compare the methods. As the results show that our approach outperforms the other 7 methods on 2 out of the 4 datasets, we also discuss the potential reasons of the observed behaviour.

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Author information

Authors and Affiliations

  1. Department of Computing and Informatics, Bournemouth University, Fern Barrow, Poole, BH12 5BB, UK

    Akanda Wahid -Ul- Ashraf & Marcin Budka

  2. School of Software, University of Technology Sydney, Broadway, PO Box 123, Sydney, NSW, 2007, Australia

    Katarzyna Musial-Gabrys

Authors
  1. Akanda Wahid -Ul- Ashraf
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  2. Marcin Budka
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  3. Katarzyna Musial-Gabrys
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Corresponding author

Correspondence to Akanda Wahid -Ul- Ashraf .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Wahid -Ul- Ashraf, A., Budka, M., Musial-Gabrys, K. (2018). Newton’s Gravitational Law for Link Prediction in Social Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

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