Data sharing is necessary for AI to be widely used, but sharing sensitive data with others with privacy is risky. To solve these problems, it is necessary to synthesize realistic tabular data. In many cases, tabular data contains a mixture of continuous, mixed, categorical columns. Moreover, columns of the same type may have multimodal distribution or be highly imbalanced. These issues make it challenging to synthesize tabular data. The synthesized tabular data should reflect the relational meaning between columns of tabular data, so modeling the probability distribution of tabular data is a non-trivial task. Traditional tabular data synthesizing models are based on GAN or diffusion models and are built using fully connected or convolutional layers. However, fully connected layers have the disadvantage of low inductive bias, and convolutional layers are not invariant to the column order of tabular data. Therefore, we assume that converting tabular data into graph-structured data and using a graph neural network would produce better synthetic data than using fully connected layers or convolutional layers. Our study aims to show that GANs constructed with graph neural networks can outperform existing GAN models using fully connected layers or convolutional layers. We propose CG-TGAN, a conditional GAN built using graph neural networks. To learn how to synthesize realistic data, the graph neural networks in the discriminator and generator learn graph-level tasks and node-level tasks together. The discriminator of CG-TGAN learns a graph-level task to distinguish between real and synthetic data and node-level tasks to predict the value of the target node. CG-TGAN’s generator learns a graph-level task to synthesize an overall graph similar to real data and node-level tasks to learn how to synthesize a fake graph with the proper relation between nodes. In this paper, we show that CG-TGAN outperforms GAN-based models and is comparable to diffusion-based models.