# DYMOND: DYnamic MOtif-NoDes Network Generative Model

arXiv: https://arxiv.org/abs/2308.00770 · HTML: https://mlsanity.com/paper/2308.00770/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Simplicial Closure and higher-order link prediction](https://mlsanity.com/paper/1802.06916/) (2018)
- [Modern temporal network theory: A colloquium](https://mlsanity.com/paper/1508.01303/) (2015)
- [Exploring the structure and function of temporal networks with dynamic graphlets](https://mlsanity.com/paper/1412.3885/) (2014)
- [Epidemiologically optimal static networks from temporal network data](https://mlsanity.com/paper/1302.0692/) (2013)
- [How memory generates heterogeneous dynamics in temporal networks](https://mlsanity.com/paper/1409.1805/) (2014)
- [Burstiness and aging in social temporal networks](https://mlsanity.com/paper/1412.0587/) (2014)
- [From calls to communities: a model for time varying social networks](https://mlsanity.com/paper/1506.00393/) (2015)

## Built on by (most cited first, 11 of 11)

- [HOG-Diff: Higher-Order Guided Diffusion for Graph Generation](https://mlsanity.com/paper/2502.04308/) (2025)
- [Efficient Dynamic Attributed Graph Generation](https://mlsanity.com/paper/2412.08810/) (2024)
- [A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation](https://mlsanity.com/paper/2412.15582/) (2024)
- [A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History](https://mlsanity.com/paper/2501.06485/) (2025)
- [GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning](https://mlsanity.com/paper/2507.03267/) (2025)
- [Quality Measures for Dynamic Graph Generative Models](https://mlsanity.com/paper/2503.01720/) (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)
- [Fine-Grained Graph Generation through Latent Mixture Scheduling](https://mlsanity.com/paper/2605.02780/) (2026)
- [When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction](https://mlsanity.com/paper/2607.24662/) (2026)
- [A temporal graph dissimilarity measure capturing epidemic spreading behavior](https://mlsanity.com/paper/2610.16456/) (2026)

## Abstract (arXiv)

Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longer-range correlations in connections and activity. In spite of this, there are few generative graph models that consider higher-order network structures and even fewer that focus on using motifs in models of dynamic graphs. Most existing generative models for temporal graphs strictly grow the networks via edge addition, and the models are evaluated using static graph structure metrics -- which do not adequately capture the temporal behavior of the network. To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND) -- a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node plays the hub role in a wedge, while the remaining two act as spokes). We compare DYMOND to three dynamic graph generative model baselines on real-world networks and show that DYMOND performs better at generating graph structure and node behavior similar to the observed network. We also propose a new methodology to adapt graph structure metrics to better evaluate the temporal aspect of the network. These metrics take into account the changes in overall graph structure and the individual nodes' behavior over time.