Graph generative models are highly important for sharing surrogate data and benchmarking purposes. Real-world complex systems often exhibit dynamic nature, where the interactions among nodes change over time in the form of a temporal network. Most temporal network generation models extend the static graph generation models by incorporating temporality in the generation process. More recently, temporal motifs are used to generate temporal networks with better success. However, existing models are often restricted to a small set of predefined motif patterns due to the high computational cost of counting temporal motifs. In this work, we develop a practical temporal graph generator, Motif Transition Model (MTM), to generate synthetic temporal networks with realistic global and local features. Our key idea is modeling the arrival of new events as temporal motif transition processes. We first calculate the transition properties from the input graph and then simulate the motif transition processes based on the transition probabilities and transition rates. We demonstrate that our model consistently outperforms the baselines with respect to preserving various global and local temporal graph statistics and runtime performance.

翻译：图生成模型对于共享替代数据和基准测试目的至关重要。现实世界的复杂系统通常表现出动态特性，其中节点间的交互以时间网络的形式随时间变化。大多数时间网络生成模型通过在生成过程中融入时间性来扩展静态图生成模型。近期，时间模体被用于更成功地生成时间网络。然而，由于计算时间模体的高成本，现有模型往往局限于少量的预定义模体模式。在本工作中，我们开发了一种实用的时间图生成器——模体转换模型（MTM），用于生成具有真实全局和局部特征的合成时间网络。我们的核心思想是将新事件的到达建模为时间模体转换过程。我们首先计算输入图的转换属性，然后基于转换概率和转换率模拟模体转换过程。我们证明，在保留各种全局和局部时间图统计量以及运行时性能方面，我们的模型始终优于基线模型。