# Restricted Tweedie Stochastic Block Models

arXiv: https://arxiv.org/abs/2310.10952 · HTML: https://mlsanity.com/paper/2310.10952/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [Learning Latent Block Structure in Weighted Networks](https://mlsanity.com/paper/1404.0431/) (2014)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [A goodness-of-fit test for stochastic block models](https://mlsanity.com/paper/1412.4857/) (2014)
- [Nonparametric weighted stochastic block models](https://mlsanity.com/paper/1708.01432/) (2017)
- [Determining the Number of Communities in Degree-corrected Stochastic Block Models](https://mlsanity.com/paper/1809.01028/) (2018)
- [Change Point Estimation in a Dynamic Stochastic Block Model](https://mlsanity.com/paper/1812.03090/) (2018)
- [Estimating the number of communities in networks by spectral methods](https://mlsanity.com/paper/1507.00827/) (2015)
- [Pairwise Covariates-adjusted Block Model for Community Detection](https://mlsanity.com/paper/1807.03469/) (2018)
- [Fast Network Community Detection with Profile-Pseudo Likelihood Methods](https://mlsanity.com/paper/2011.00647/) (2020)
- [Adapting the Stochastic Block Model to Edge-Weighted Networks](https://mlsanity.com/paper/1305.5782/) (2013)
- [Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis](https://mlsanity.com/paper/1806.03829/) (2018)
- [Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer Parallelizable Algorithm with Communication](https://mlsanity.com/paper/1610.09724/) (2016)

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

- [Near-Universal Multiplicative Updates for Nonnegative Einsum Factorization](https://mlsanity.com/paper/2602.02759/) (2026)
- [Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows](https://mlsanity.com/paper/2606.17267/) (2026)

## Abstract (arXiv)

The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of non-negative zero-inflated continuous edge weights. To model the international trading network, where edge weights represent trading values between countries, we propose an innovative SBM based on a restricted Tweedie distribution. Additionally, we incorporate nodal information, such as the geographical distance between countries, and account for its dynamic effect on edge weights. Notably, we show that given a sufficiently large number of nodes, estimating this covariate effect becomes independent of community labels of each node when computing the maximum likelihood estimator of parameters in our model. This result enables the development of an efficient two-step algorithm that separates the estimation of covariate effects from other parameters. We demonstrate the effectiveness of our proposed method through extensive simulation studies and an application to real-world international trading data.