# Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC

arXiv: https://arxiv.org/abs/2403.00044 · HTML: https://mlsanity.com/paper/2403.00044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [A Complete Recipe for Stochastic Gradient MCMC](https://mlsanity.com/paper/1506.04696/) (2015)
- [Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks](https://mlsanity.com/paper/1512.07666/) (2015)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Scalable Bayes via Barycenter in Wasserstein Space](https://mlsanity.com/paper/1508.05880/) (2015)
- [Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations](https://mlsanity.com/paper/1606.01855/) (2016)
- [Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction](https://mlsanity.com/paper/1501.06218/) (2015)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [Gamma Belief Networks](https://mlsanity.com/paper/1512.03081/) (2015)
- [Leveraging Node Attributes for Incomplete Relational Data](https://mlsanity.com/paper/1706.04289/) (2017)
- [Stochastic Gradient MCMC with Stale Gradients](https://mlsanity.com/paper/1610.06664/) (2016)
- [Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC](https://mlsanity.com/paper/1706.01724/) (2017)
- [Locally Private Bayesian Inference for Count Models](https://mlsanity.com/paper/1803.08471/) (2018)
- [Dynamic Poisson Factorization](https://mlsanity.com/paper/1509.04640/) (2015)
- [Scaling up Dynamic Topic Models](https://mlsanity.com/paper/1602.06049/) (2016)
- [Scalable MCMC for Mixed Membership Stochastic Blockmodels](https://mlsanity.com/paper/1510.04815/) (2015)
- [Nonparametric Bayesian Negative Binomial Factor Analysis](https://mlsanity.com/paper/1604.07464/) (2016)
- [A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks](https://mlsanity.com/paper/1805.11054/) (2018)
- [The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks](https://mlsanity.com/paper/2006.12952/) (2020)
- [Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport](https://mlsanity.com/paper/2212.14527/) (2022)
- [Nonparametric Multi-group Membership Model for Dynamic Networks](https://mlsanity.com/paper/1311.2079/) (2013)
- [Deep Generative Models for Relational Data with Side Information](https://mlsanity.com/paper/1706.05136/) (2017)
- [Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling](https://mlsanity.com/paper/1410.7812/) (2014)
- [On the Model Shrinkage Effect of Gamma Process Edge Partition Models](https://mlsanity.com/paper/1709.08770/) (2017)
- [Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis](https://mlsanity.com/paper/1809.03566/) (2018)

## Abstract (arXiv)

The edge partition model (EPM) is a generative model for extracting an overlapping community structure from static graph-structured data. In the EPM, the gamma process (GaP) prior is adopted to infer the appropriate number of latent communities, and each vertex is endowed with a gamma distributed positive memberships vector. Despite having many attractive properties, inference in the EPM is typically performed using Markov chain Monte Carlo (MCMC) methods that prevent it from being applied to massive network data. In this paper, we generalize the EPM to account for dynamic enviroment by representing each vertex with a positive memberships vector constructed using Dirichlet prior specification, and capturing the time-evolving behaviour of vertices via a Dirichlet Markov chain construction. A simple-to-implement Gibbs sampler is proposed to perform posterior computation using Negative- Binomial augmentation technique. For large network data, we propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in the proposed model. The experimental results show that the novel methods achieve competitive performance in terms of link prediction, while being much faster.