# Using Motif Transitions for Temporal Graph Generation

arXiv: https://arxiv.org/abs/2306.11190 · HTML: https://mlsanity.com/paper/2306.11190/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Statistical clustering of temporal networks through a dynamic stochastic block model](https://mlsanity.com/paper/1506.07464/) (2015)
- [A Scalable Generative Graph Model with Community Structure](https://mlsanity.com/paper/1302.6636/) (2013)
- [Exploring the structure and function of temporal networks with dynamic graphlets](https://mlsanity.com/paper/1412.3885/) (2014)
- [When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks](https://mlsanity.com/paper/1702.05499/) (2017)
- [Modeling sequences and temporal networks with dynamic community structures](https://mlsanity.com/paper/1509.04740/) (2015)
- [Temporal Network Motifs: Models, Limitations, Evaluation](https://mlsanity.com/paper/2005.11817/) (2020)
- [Detectability thresholds and optimal algorithms for community structure in dynamic networks](https://mlsanity.com/paper/1506.06179/) (2015)
- [Temporal motifs reveal homophily, gender-specific patterns and group talk in mobile communication networks](https://mlsanity.com/paper/1302.2563/) (2013)
- [Random graph models for dynamic networks](https://mlsanity.com/paper/1607.07570/) (2016)
- [Epidemiologically optimal static networks from temporal network data](https://mlsanity.com/paper/1302.0692/) (2013)
- [Dynamic stochastic blockmodels: Statistical models for time-evolving networks](https://mlsanity.com/paper/1304.5974/) (2013)
- [Nonparametric Multi-group Membership Model for Dynamic Networks](https://mlsanity.com/paper/1311.2079/) (2013)
- [Statistically validated mobile communication networks: Evolution of motifs in European and Chinese data](https://mlsanity.com/paper/1403.3785/) (2014)
- [Temporal Motifs in Patent Opposition and Collaboration Networks](https://mlsanity.com/paper/2110.11198/) (2021)
- [Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms](https://mlsanity.com/paper/2301.07791/) (2023)
- [ITeM: Independent Temporal Motifs to Summarize and Compare Temporal Networks](https://mlsanity.com/paper/2002.08312/) (2020)

## Built on by (most cited first, 7 of 7)

- [Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning](https://mlsanity.com/paper/2401.01232/) (2024)
- [GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2603.19677/) (2026)
- [A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2505.04461/) (2025)
- [ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks](https://mlsanity.com/paper/2508.20829/) (2025)
- [Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs](https://mlsanity.com/paper/2504.15979/) (2025)
- [Efficient Learning-based Graph Simulation for Temporal Graphs](https://mlsanity.com/paper/2510.05569/) (2025)
- [Stochastic Event Prediction via Temporal Motif Transitions](https://mlsanity.com/paper/2603.05874/) (2026)

## Abstract (arXiv)

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.