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Link prediction based on linear dynamical response

Author

Listed:
  • Gao, Hua
  • Huang, Jianbin
  • Cheng, Qiang
  • Sun, Heli
  • Wang, Baoli
  • Li, He

Abstract

Link prediction has attracted increasing research attention recently, which aims to predict missing links in complex networks. However, the existing link prediction methods are primarily based on network structures alone, which are incapable of capturing the dynamics defined on top of the fixed network structures. In this paper, we introduce a linear dynamical response-based similarity measure between nodes into link prediction task. To address the efficiency problem, we design a new iterative procedure to avoid the explicit computation of linear dynamical response (LDR) index. Empirically, we conduct extensive experiments on real networks from various fields. The results show that LDR index leads to promising predicting performance for link prediction.

Suggested Citation

  • Gao, Hua & Huang, Jianbin & Cheng, Qiang & Sun, Heli & Wang, Baoli & Li, He, 2019. "Link prediction based on linear dynamical response," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119308179
    DOI: 10.1016/j.physa.2019.121397
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    Citations

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    Cited by:

    1. Aziz, Furqan & Gul, Haji & Muhammad, Ishtiaq & Uddin, Irfan, 2020. "Link prediction using node information on local paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.

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