# A Generative Model for Dynamic Networks with Applications

arXiv: https://arxiv.org/abs/1802.03725 · HTML: https://mlsanity.com/paper/1802.03725/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting](https://mlsanity.com/paper/2112.10154/) (2021)
- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation](https://mlsanity.com/paper/2508.12674/) (2025)

## Abstract (arXiv)

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.