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DNETC: dynamic network embedding preserving both triadic closure evolution and community structures

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Abstract

Network embedding, a central issue of deep learning preprocessing on social networks, aims to transform network elements (vertices) into low-dimensional latent vector space while preserving the topology and properties of the network. However, most of the existing methods mainly focus on static networks, neglecting the dynamic characteristics of real social networks. The explanation for the fundamental dynamic mechanism of social network evolution is still lacking. We design a novel dynamic network embedding approach preserving both triadic closure evolution and community structures (DNETC). First, three factors, the popularity of vertices, the proximity of vertices, and the community structures, are incorporated relying on the triadic closure principle in social networks. Second, the triadic closure loss function, the community loss function, and the temporal smoothness loss function are constructed and incorporated to optimize DNETC. Finally, the low-dimensional cognition presentation of a dynamic social network can be achieved, which can save both the evolution patterns of microscopic vertices and the structure information of macroscopic communities. Experiments on the classical tasks of link prediction, link reconstruction, and changed link reconstruction and prediction demonstrate the superiority of DNETC over state-of-the-art methods. The first experimental results validate the effectiveness of adopting triadic closure progress and community structures to improve the quality of the learned low-dimensional vectors. The last experimental results further verify the parameter sensitivity of DNETC to the analysis task. It provides a new idea for dynamic network embedding to reflect the real evolution characteristics of networks and enhance the effect of network analysis tasks. The code is available at https://github.com/YangMin-10/DNETC.

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Code availability

The code is available at https://github.com/YangMin-10/DNETC.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant Numbers 61902324, 11426179, and 61872298), the Science and Technology Program of Sichuan Province (Grant Numbers 2023ZDYF2732, 2021YFQ0008, 2020JDRC0067).

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Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

    Min Yang, Xiaoliang Chen & Yajun Du

  2. Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, H3C3J7, Canada

    Xiaoliang Chen & Peng Lu

  3. College of Computer Science, Sichuan University, Chengdu, 610065, China

    Baiyang Chen

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  1. Min Yang
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  2. Xiaoliang Chen
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  3. Baiyang Chen
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  4. Peng Lu
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  5. Yajun Du
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Contributions

MY involved in conceptualization; MY involved in data curation; XLC involved in formal analysis; PL involved in funding acquisition; MY involved in investigation; XLC involved in methodology; YD involved in project administration; XLC involved in resources; MY involved in software; XLC involved in supervision; BYC involved in validation; MY involved in visualization; MY involved in writing—original draft; and XLC involved in writing—review and editing.

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Correspondence to Xiaoliang Chen.

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Yang, M., Chen, X., Chen, B. et al. DNETC: dynamic network embedding preserving both triadic closure evolution and community structures. Knowl Inf Syst 65, 1129–1157 (2023). https://doi.org/10.1007/s10115-022-01792-4

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