Using Motif Transitions for Temporal Graph Generation
Using Motif Transitions for Temporal Graph Generation
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DOI：
10.1145/3580305.3599540
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Penghang Liu;Ahmet Erdem Sarıyüce
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文献类型：
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作者：
Penghang Liu;Ahmet Erdem Sarıyüce
图生成模型对于共享替代数据和基准测试目的非常重要。现实世界的复杂系统往往表现出动态的性质，其中节点之间的相互作用随着时间的推移而变化，以时间网络的形式。大多数时态网络生成模型通过在生成过程中加入时态性来扩展静态图生成模型。最近，时间模体被用来生成时间网络，取得了更好的成功。然而，现有的模型往往被限制到一个小的预定义的主题模式，由于计算时间的图案的高计算成本。在这项工作中，我们开发了一个实用的时间图生成器，Motif转换模型（MTM），生成具有现实的全球和本地功能的合成时间网络。我们的核心思想是将新事件的到来建模为时间母题转换过程。我们首先从输入图计算过渡属性，然后根据过渡概率和过渡率模拟模体过渡过程。我们证明了我们的模型在保持各种全局和局部时间图统计数据和运行时性能方面始终优于基线。
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.