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2022

Row Conditional-TGAN for generating synthetic relational databases

Gueye, Mohamed, Attabi, Yazid, Dumas, Maxime

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Besides reproducing tabular data properties of standalone tables, synthetic relational databases also require modeling the relationships between related tables.

In this paper, we propose the Row Conditional-Tabular Generative Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN) model that extends the tabular GAN to support modeling and synthesizing relational databases.
The RC-TGAN models relationship information between tables by incorporating conditional data of parent rows into the design of the child table's GAN.
We further extend the RC-TGAN to model the influence that grandparent table rows may have on their grandchild rows, in order to prevent the loss of this connection when the rows of the parent table fail to transfer this relationship information.

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