# Dynamic stochastic blockmodels for time-evolving social networks

arXiv: https://arxiv.org/abs/1403.0921 · HTML: https://mlsanity.com/paper/1403.0921/

## Key facts

- Cited by 20 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Mar 2014.

## Built on by (most cited first, 20 of 20)

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- [Spectral embedding for dynamic networks with stability guarantees](https://mlsanity.com/paper/2106.01282/) (2021)
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- [Valid Conformal Prediction for Dynamic GNNs](https://mlsanity.com/paper/2405.19230/) (2024)
- [Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks](https://mlsanity.com/paper/2306.06155/) (2023)
- [A Simple and Powerful Framework for Stable Dynamic Network Embedding](https://mlsanity.com/paper/2311.09251/) (2023)
- [Structured Optimal Variational Inference for Dynamic Latent Space Models](https://mlsanity.com/paper/2209.15117/) (2022)
- [Restricted Tweedie Stochastic Block Models](https://mlsanity.com/paper/2310.10952/) (2023)
- [Learning User Embeddings from Temporal Social Media Data: A Survey](https://mlsanity.com/paper/2105.07996/) (2021)
- [Systematic assessment of the quality of fit of the stochastic block model for empirical networks](https://mlsanity.com/paper/2201.01658/) (2022)
- [Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis](https://mlsanity.com/paper/2208.01365/) (2022)
- [Latent Evolution Model for Change Point Detection in Time-varying Networks](https://mlsanity.com/paper/2212.08818/) (2022)
- [A Probit Tensor Factorization Model For Relational Learning](https://mlsanity.com/paper/2111.03943/) (2021)
- [Recurrent segmentation meets block models in temporal networks](https://mlsanity.com/paper/2205.09862/) (2022)
- [Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks](https://mlsanity.com/paper/2304.05894/) (2023)
- [Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC](https://mlsanity.com/paper/2403.00044/) (2024)
- [Tensor State Space-based Dynamic Multilayer Network Modeling](https://mlsanity.com/paper/2506.02413/) (2025)
- [A Mean Field Games Perspective on Evolutionary Clustering](https://mlsanity.com/paper/2603.27137/) (2026)
- [Nonparametric estimation of time-varying network connections by multi-stage smoothing](https://mlsanity.com/paper/2605.06862/) (2026)
- [ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection](https://mlsanity.com/paper/2608.16906/) (2026)

## Abstract (arXiv)

Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks. Most existing work has focused on modeling static networks, which represent either a single time snapshot or an aggregate view over time. There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena. In this paper, we present a state-space model for dynamic networks that extends the well-known stochastic blockmodel for static networks to the dynamic setting. We fit the model in a near-optimal manner using an extended Kalman filter (EKF) augmented with a local search. We demonstrate that the EKF-based algorithm performs competitively with a state-of-the-art algorithm based on Markov chain Monte Carlo sampling but is significantly less computationally demanding.