# A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History

arXiv: https://arxiv.org/abs/2501.06485 · HTML: https://mlsanity.com/paper/2501.06485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations](https://mlsanity.com/paper/2202.02514/) (2022)
- [Permutation Invariant Graph Generation via Score-Based Generative Modeling](https://mlsanity.com/paper/2003.00638/) (2020)
- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [DeFoG: Discrete Flow Matching for Graph Generation](https://mlsanity.com/paper/2410.04263/) (2024)
- [Discrete-state Continuous-time Diffusion for Graph Generation](https://mlsanity.com/paper/2405.11416/) (2024)
- [Random walks and diffusion on networks](https://mlsanity.com/paper/1612.03281/) (2016)
- [DreamDA: Generative Data Augmentation with Diffusion Models](https://mlsanity.com/paper/2403.12803/) (2024)
- [A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot](https://mlsanity.com/paper/2307.14397/) (2023)
- [TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs](https://mlsanity.com/paper/2203.03564/) (2022)
- [DYMOND: DYnamic MOtif-NoDes Network Generative Model](https://mlsanity.com/paper/2308.00770/) (2023)
- [Robust Knowledge Adaptation for Dynamic Graph Neural Networks](https://mlsanity.com/paper/2207.10839/) (2022)
- [Ranking in evolving complex networks](https://mlsanity.com/paper/1704.08027/) (2017)
- [Reconstructing the evolution history of networked complex systems](https://mlsanity.com/paper/2403.14983/) (2024)
- [Conditions for viral influence spreading through multiplex correlated social networks](https://mlsanity.com/paper/1404.3114/) (2014)
- [TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics](https://mlsanity.com/paper/2408.09825/) (2024)

## Built on by (most cited first, 2 of 2)

- [Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data](https://mlsanity.com/paper/2502.17928/) (2025)
- [Inferring Network Evolutionary History via Structure-State Coupled Learning](https://mlsanity.com/paper/2601.02121/) (2026)

## Abstract (arXiv)

The evolutionary processes of complex systems contain critical information regarding their functional characteristics. The generation time of edges provides insights into the historical evolution of various networked complex systems, such as protein-protein interaction networks, ecosystems, and social networks. Recovering these evolutionary processes holds significant scientific value, including aiding in the interpretation of the evolution of protein-protein interaction networks. However, existing methods are capable of predicting the generation times of remaining edges given a partial temporal network but often perform poorly in cross-network prediction tasks. These methods frequently fail in edge generation time recovery tasks for static networks that lack timestamps. In this work, we adopt a comparative paradigm-based framework that fuses multiple networks for training, enabling cross-network learning of the relationship between network structure and edge generation times. Compared to separate training, this approach yields an average accuracy improvement of 16.98%. Furthermore, given the difficulty in collecting temporal networks, we propose a novel diffusion-model-based generation method to produce a large number of temporal networks. By combining real temporal networks with generated ones for training, we achieve an additional average accuracy improvement of 5.46% through joint training.