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Dynamic bipartite network model based on structure and preference features

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Abstract

Based on the complex network, the relationship in the real complex system can be modeled, and the bipartite network is a special complex network, which can describe the complex system containing two kinds of objects. Although existing bipartite networks can model complex systems, conventional methods are restricted to a couple of limitations. (1) The dynamic interaction between nodes cannot be described over time. (2) The implicit features of nodes in the network cannot be effectively mined. Based on these, this paper proposes a dynamic bipartite network model (DBN) to model the dynamic interaction between two types of objects in real complex systems, and mine the structure features and preference features of nodes in the network. First, the dynamic interaction between two types of objects in a complex system is modeled as a dynamic bipartite network, which can reflect the interaction between objects in each time slice. Then, the structure features and preference features of each time slice are mined based on the dynamic bipartite network, where the structure features reflect the dynamic structural changes of the nodes, and the preference features reflect the potential preferences of the nodes. Finally, the features of each time slice are fused and input into the gate recurrent unit model to predict the interaction between nodes. Extensive experiments are performed on a large-scale real complex system. The results show that DBN significantly outperforms state-of-the-art prediction methods in terms of multiple evaluation metrics.

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Data availability

The dataset and code for this study can be found at https://github.com/lvhehe/DBN.

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Acknowledgements

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Funding

The author acknowledges the support provided by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (NoN2022D01A236).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Hehe Lv, Guobing Zou, Shengxiang Hu, Chenyang Zhou & Liangrui Wu

  2. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

    Bofeng Zhang

Authors
  1. Hehe Lv
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  2. Guobing Zou
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  3. Bofeng Zhang
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  4. Shengxiang Hu
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  5. Chenyang Zhou
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  6. Liangrui Wu
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Contributions

HL proposed the research plan, coding, and writing of this paper. GZ and BZ supervised the projects. SH, CZ, and LW were in charge of data collection and analysis, translation, and discussion. All authors contributed to the article and approved the submitted version.

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Correspondence to Guobing Zou.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Not applicable as this study did not involve human participants.

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Lv, H., Zou, G., Zhang, B. et al. Dynamic bipartite network model based on structure and preference features. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02093-8

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