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Nearest neighbor walk network embedding for link prediction in complex networks

Author

Listed:
  • Zhou, Mingqiang
  • Han, Qizhi
  • Li, Mengjiao
  • Li, Kunpeng
  • Qian, Zhiyuan

Abstract

Link prediction is an important and challenging task in complex network analysis, which aims to find missing links or predict possible links in network, and the link prediction method based on network embedding has received extensive attention. This paper proposes a network embedding with nearest neighbor walking and its link prediction method, to solve the problem that the network embedding does not consider the difference of neighbor nodes when generating node sequences. The method is called as nearest neighbor walk network embedding for link prediction, which first uses natural nearest neighbor on network to find the nearest neighbor of nodes, then measures the contribution of nearest neighbors to network embedding by clustering coefficient to generate node sequences, and forms the network embedding applied to link prediction. Experiments on real networks such as Cora, Citeseer, etc. show the method has higher accuracy.

Suggested Citation

  • Zhou, Mingqiang & Han, Qizhi & Li, Mengjiao & Li, Kunpeng & Qian, Zhiyuan, 2023. "Nearest neighbor walk network embedding for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 620(C).
    • Handle: RePEc:eee:phsmap:v:620:y:2023:i:c:s0378437123003126
    DOI: 10.1016/j.physa.2023.128757
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