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Structural link prediction based on ant colony approach in social networks

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  • Sherkat, Ehsan
  • Rahgozar, Maseud
  • Asadpour, Masoud

Abstract

As the size and number of online social networks are increasing day by day, social network analysis has become a popular issue in many branches of science. The link prediction is one of the key rolling issues in the analysis of social network’s evolution. As the size of social networks is increasing, the necessity for scalable link prediction algorithms is being felt more. The aim of this paper is to introduce a new unsupervised structural link prediction algorithm based on the ant colony approach. Recently, ant colony approach has been used for solving some graph problems. Different kinds of networks are used for testing the proposed approach. In some networks, the proposed scalable algorithm has the best result in comparison to other structural unsupervised link prediction algorithms. In order to evaluate the algorithm results, methods like the top-n precision, area under the Receiver Operating Characteristic (ROC) and Precision–Recall curves are carried out on real-world networks.

Suggested Citation

  • Sherkat, Ehsan & Rahgozar, Maseud & Asadpour, Masoud, 2015. "Structural link prediction based on ant colony approach in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 80-94.
    • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:80-94
    DOI: 10.1016/j.physa.2014.10.011
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    Citations

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    Cited by:

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    10. Liu, Sen & Dong, Zhiliang & Ding, Chao & Wang, Tian & Zhang, Yichi, 2020. "Do you need cobalt ore? Estimating potential trade relations through link prediction," Resources Policy, Elsevier, vol. 66(C).

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