Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic. However, despite its significance, conditional graph generation that creates graphs with desired features is relatively less explored in previous studies. This paper addresses the problem of class-conditional graph generation that uses class labels as generation constraints by introducing the Class Conditioned Graph Generator (CCGG). We built CCGG by injecting the class information as an additional input into a graph generator model and including a classification loss in its total loss along with a gradient passing trick. Our experiments show that CCGG outperforms existing conditional graph generation methods on various datasets. It also manages to maintain the quality of the generated graphs in terms of distribution-based evaluation metrics.
翻译:图表数据结构是研究关联实体的基础。随着数据以图表形式呈现的应用程序数量的增加,图表生成问题最近已成为一个热题。然而,尽管其意义重大,在以往的研究中,创造具有理想特征的图表的有条件图形生成相对较少探讨。本文件通过采用“分类限定图形生成器”(CCGG),解决使用类别标签作为生成限制的类别有条件图形生成问题。我们通过将类别信息作为附加投入注入一个图形生成器模型,将分类损失与梯度传递技巧一起纳入总损失。我们的实验显示,CGG在各种数据集中超越了现有的有条件的图形生成方法,它还设法保持了生成的图表在基于分布的评估指标方面的质量。