A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic technologies in repeated simulations of networks and prediction of topology changes. Although existing generative models for graphs are useful for providing graphs similar to real-world graphs, graph generation models with tunable features have been less explored in the field. Previously, we have proposed GraphTune, a generative model for graphs that continuously tune specific graph features of generated graphs while maintaining most of the features of a given graph dataset. However, the tuning accuracy of graph features in GraphTune has not been sufficient for practical applications. In this paper, we propose a method to improve the accuracy of GraphTune by adding a new mechanism to feed back errors of graph features of generated graphs and by training them alternately and independently. Experiments on a real-world graph dataset showed that the features in the generated graphs are accurately tuned compared with conventional models.

翻译：图是一种非常常见且强大的数据结构，常用于建模通信网络和社交网络。能够生成具有任意特征的图模型，是网络迭代模拟和拓扑结构变化预测中的关键技术。尽管现有的图生成模型在生成与真实世界图相似的图方面很有用，但具有可调特征的图生成模型在该领域的研究还不够深入。此前，我们提出了GraphTune——一种能够连续调节生成图的特定图特征，同时保持给定图数据集大部分特征的图生成模型。然而，GraphTune在图特征调节精度方面尚未满足实际应用需求。本文通过引入一种新机制来反馈生成图特征的误差，并交替独立训练这些特征，提出了一种提高GraphTune精度的方法。在真实世界图数据集上的实验表明，与传统模型相比，生成图中的特征得到了更精确的调节。