  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.

Social and Information Networks;Artificial Intelligence