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A link prediction method for heterogeneous networks based on BP neural network

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  • Li, Ji-chao
  • Zhao, Dan-ling
  • Ge, Bing-Feng
  • Yang, Ke-Wei
  • Chen, Ying-Wu

Abstract

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease–gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene–disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

Suggested Citation

  • Li, Ji-chao & Zhao, Dan-ling & Ge, Bing-Feng & Yang, Ke-Wei & Chen, Ying-Wu, 2018. "A link prediction method for heterogeneous networks based on BP neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 1-17.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:1-17
    DOI: 10.1016/j.physa.2017.12.018
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    References listed on IDEAS

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    1. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    2. Liu, Yangyang & Zhao, Chengli & Wang, Xiaojie & Huang, Qiangjuan & Zhang, Xue & Yi, Dongyun, 2016. "The degree-related clustering coefficient and its application to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 24-33.
    3. Li, Jichao & Ge, Bingfeng & Yang, Kewei & Chen, Yingwu & Tan, Yuejin, 2017. "Meta-path based heterogeneous combat network link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 507-523.
    4. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
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