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Toward link predictability of bipartite networks based on structural enhancement and structural perturbation

Author

Listed:
  • Chen, Xue
  • Jiao, Pengfei
  • Yu, Yandong
  • Li, Xiaoming
  • Tang, Minghu

Abstract

Link prediction in bipartite networks is attracting tremendous research interests. Most previous studies mainly assume the generation of link follows a predefined prior mechanism while neglecting complexity of the link generation mechanisms. To address this limitation, we present a parameter-free method, termed Structural Enhancement and Structural Perturbation (SESP), which jointly exploits explicit relations (low-order information) and implicit relations (high-order information) from the perspective of perturbation. The essence of SESP is that it transforms bipartite link prediction into monopartite link prediction without losing any information and predicts the missing links from a perturbed perspective. Compared with traditional link prediction methods, SESP does not assume a particular link generation mechanism, but learns this mechanism from the network itself. Extensive experiments on several disparate real-world bipartite networks demonstrate the effectiveness of the SESP model.

Suggested Citation

  • Chen, Xue & Jiao, Pengfei & Yu, Yandong & Li, Xiaoming & Tang, Minghu, 2019. "Toward link predictability of bipartite networks based on structural enhancement and structural perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306570
    DOI: 10.1016/j.physa.2019.121072
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