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Link direction for link prediction

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  • Shang, Ke-ke
  • Small, Michael
  • Yan, Wei-sheng

Abstract

Almost all previous studies on link prediction have focused on using the properties of the network to predict the existence of links between pairs of nodes. Unfortunately, previous methods rarely consider the role of link direction for link prediction. In fact, many real-world complex networks are directed and ignoring the link direction will mean overlooking important information. In this study, we propose a phase-dynamic algorithm of the directed network nodes to analyse the role of link directions and demonstrate that the bi-directional links and the one-directional links have different roles in link prediction and network structure formation. From this, we propose new directional prediction methods and use six real networks to test our algorithms. In real networks, we find that compared to a pair of nodes which are connected by a one-directional link, a pair of nodes which are connected by a bi-directional link always have higher probabilities to connect to the common neighbours with only bi-directional links (or conversely by one-directional links). We suggest that, in the real networks, the bi-directional links will generally be more informative for link prediction and network structure formation. In addition, we propose a new directional randomized algorithm to demonstrate that the direction of the links plays a significant role in link prediction and network structure formation.

Suggested Citation

  • Shang, Ke-ke & Small, Michael & Yan, Wei-sheng, 2017. "Link direction for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 767-776.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:767-776
    DOI: 10.1016/j.physa.2016.11.129
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    References listed on IDEAS

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    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
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    Cited by:

    1. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
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    3. Aghabozorgi, Farshad & Khayyambashi, Mohammad Reza, 2018. "A new similarity measure for link prediction based on local structures in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 12-23.
    4. Yin, Likang & Zheng, Haoyang & Bian, Tian & Deng, Yong, 2017. "An evidential link prediction method and link predictability based on Shannon entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 699-712.

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