# Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks

arXiv: https://arxiv.org/abs/2304.05894 · HTML: https://mlsanity.com/paper/2304.05894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [On Interpretability of Artificial Neural Networks: A Survey](https://mlsanity.com/paper/2001.02522/) (2020)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [A Review of Stochastic Block Models and Extensions for Graph Clustering](https://mlsanity.com/paper/1903.00114/) (2019)
- [Statistical clustering of temporal networks through a dynamic stochastic block model](https://mlsanity.com/paper/1506.07464/) (2015)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [A semiparametric extension of the stochastic block model for longitudinal networks](https://mlsanity.com/paper/1512.07075/) (2015)
- [Interactions in information spread: quantification and interpretation using stochastic block models](https://mlsanity.com/paper/2004.04552/) (2020)
- [Accurate and scalable social recommendation using mixed-membership stochastic block models](https://mlsanity.com/paper/1604.01170/) (2016)
- [Optimal prediction of decisions and model selection in social dilemmas using block models](https://mlsanity.com/paper/2004.12151/) (2020)
- [Dynamic stochastic blockmodels: Statistical models for time-evolving networks](https://mlsanity.com/paper/1304.5974/) (2013)
- [Tensorial and bipartite block models for link prediction in layered networks and temporal networks](https://mlsanity.com/paper/1803.01616/) (2018)
- [Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies](https://mlsanity.com/paper/2103.06121/) (2021)
- [Follow the guides: disentangling human and algorithmic curation in online music consumption](https://mlsanity.com/paper/2109.03915/) (2021)
- [Dynamic Infinite Mixed-Membership Stochastic Blockmodel](https://mlsanity.com/paper/1306.2999/) (2013)

## Abstract (arXiv)

Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption of mixed-membership clustering. In this work, we propose to extend this later class of models to infer dynamic labeled networks under a mixed membership assumption. Our approach takes the form of a temporal prior on the model's parameters. It relies on the single assumption that dynamics are not abrupt. We show that our method significantly differs from existing approaches, and allows to model more complex systems --dynamic labeled networks. We demonstrate the robustness of our method with several experiments on both synthetic and real-world datasets. A key interest of our approach is that it needs very few training data to yield good results. The performance gain under challenging conditions broadens the variety of possible applications of automated learning tools --as in social sciences, which comprise many fields where small datasets are a major obstacle to the introduction of machine learning methods.