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Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration

Summary: Comprehensive experimental study of relational data synthesis with GANs in a unified framework, detailing a design space over architectures and training methods. Shows GANs' promise over traditional synthesis and offers design guidance plus future directions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12280
Venue
VLDB
Year
2020
Pagerank
6.7982037e-05
Overall Rank
4,261 | 70.77%
DOI
10.14778/3407790.3407802

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{fan_vldb20,
        title = {{Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration}},
        author = {Fan, Ju and Liu, Tongyu and Li, Guoliang and Chen, Junyou and Shen, Yuwei and Du, Xiaoyong},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {1962--1975},
        doi = {10.14778/3407790.3407802},
        url = {https://doi.org/10.14778/3407790.3407802},
        year = {2020}
}

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