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Link prediction from fusion information

Author

Listed:
  • Jiao, Yang
  • Wu, Jianshe
  • Xiang, Peng
  • Wang, Fang

Abstract

Link prediction of complex network aims at predicting the likelihood of the existence of unknown links. In the most previous works, many kinds of information, such as common neighbor, local path, transition probability, have been used to predict the unknown links. However, methods using fusion information are relatively rare. In this paper, different kinds of information are fused with community information and a method using fusion information is proposed. According to the objective function of density of modularity in the method for community detection, an index based on community information is defined and is further fused with the existing indexes based on transition probability and degrees of nodes, to predict the unknown links. Indexes based on fusion information are compared with not only frequently-used, basic indexes, but other methods using fusion information. Experimental results show that indexes based on fusion information perform efficiently and accurately.

Suggested Citation

  • Jiao, Yang & Wu, Jianshe & Xiang, Peng & Wang, Fang, 2023. "Link prediction from fusion information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    • Handle: RePEc:eee:phsmap:v:618:y:2023:i:c:s0378437123002492
    DOI: 10.1016/j.physa.2023.128694
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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