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Accuracy test for link prediction in terms of similarity index: The case of WS and BA models

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  • Ahn, Min-Woo
  • Jung, Woo-Sung

Abstract

Link prediction is a technique that uses the topological information in a given network to infer the missing links in it. Since past research on link prediction has primarily focused on enhancing performance for given empirical systems, negligible attention has been devoted to link prediction with regard to network models. In this paper, we thus apply link prediction to two network models: The Watts–Strogatz (WS) model and Barabási–Albert (BA) model. We attempt to gain a better understanding of the relation between accuracy and each network parameter (mean degree, the number of nodes and the rewiring probability in the WS model) through network models. Six similarity indices are used, with precision and area under the ROC curve (AUC) value as the accuracy metrics. We observe a positive correlation between mean degree and accuracy, and size independence of the AUC value.

Suggested Citation

  • Ahn, Min-Woo & Jung, Woo-Sung, 2015. "Accuracy test for link prediction in terms of similarity index: The case of WS and BA models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 177-183.
    • Handle: RePEc:eee:phsmap:v:429:y:2015:i:c:p:177-183
    DOI: 10.1016/j.physa.2015.01.083
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    Cited by:

    1. Zhou, Yinzuo & Wu, Chencheng & Tan, Lulu, 2021. "Biased random walk with restart for link prediction with graph embedding method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).

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