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A continuous-time diffusion model for inferring multi-layer diffusion networks

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Abstract

Inferring multilayer diffusion networks from observed cascades is both crucial and realistic. To infer multilayer diffusion networks, constructing continuous-time diffusion models that capture diffusion dynamics is a prerequisite. However, developing such models faces two main challenges: (1) reducing the number of learnable parameters for precise optimization with limited cascades while effectively modeling for accurate inference and (2) adapting the models to more realistic scenarios. In this paper, we propose a novel continuous-time diffusion model, namely the Embedding-based Continuous-time Diffusion (ECD) model, which employs an embedding method while modeling symmetric relationship strength, asymmetric relationship strength, and trust strength. Specifically, by leveraging the embedding method, the number of learnable parameters is significantly reduced compared with previous models. Then, by modeling symmetric relationship strength, our model can be used in scenarios where the relationships between nodes are symmetric. Subsequently, the trust strength can be inferred by our proposed efficient heuristic algorithm, making our model suitable for scenarios where time information is unavailable. Furthermore, we develop an optimization algorithm to optimize the proposed model and infer multilayer diffusion networks. The experimental results on synthetic and real datasets show that our model and algorithms outperform the comparison methods.

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Data Availability and Access

Some of data are available at http://www.isi.edu/~lerman/downloads/digg2009.html and http://memetracker.org/data/memetracker9.html. Some of data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is financially supported by Shenzhen Science and Technology Program under Grant No. GXWD20220817124827001 and No.JCYJ20210324132406016.

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Authors and Affiliations

  1. Harbin Institute of Technology(Shenzhen), Shenzhen, China

    Yunpeng Zhao & Hejiao Huang

  2. Harbin Institute of Technology, Shenzhen, Guangdong, 518055, China

    Xiaopeng Yao

  3. Zhejiang Key Laboratory of Social Security Big Data, Hangzhou, Zhejiang, 310000, China

    Xiaopeng Yao

Authors
  1. Yunpeng Zhao
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Contributions

Yunpeng Zhao: Investigation, conceptualization, methodology, algorithm design, theory proof, and draft writing. Xiaopeng Yao: Figure generation, algorithm improvement, time complexity and algorithm optimization, and paper revision. Hejiao Huang: Put forward ideas, discuss and optimize algorithm and method,and paper revision.

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Correspondence to Hejiao Huang.

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Data used in the study are available and do not involve ethical issues.

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Zhao, Y., Yao, X. & Huang, H. A continuous-time diffusion model for inferring multi-layer diffusion networks. Appl Intell 54, 8200–8223 (2024). https://doi.org/10.1007/s10489-024-05620-w

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