# Block Dense Weighted Networks with Augmented Degree Correction

arXiv: https://arxiv.org/abs/2105.12290 · HTML: https://mlsanity.com/paper/2105.12290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 12 of 12)

- [Community Detection in Degree-Corrected Block Models](https://mlsanity.com/paper/1607.06993/) (2016)
- [Network cross-validation by edge sampling](https://mlsanity.com/paper/1612.04717/) (2016)
- [Learning Latent Block Structure in Weighted Networks](https://mlsanity.com/paper/1404.0431/) (2014)
- [Bootstrapping Networks with Latent Space Structure](https://mlsanity.com/paper/1907.10821/) (2019)
- [Nonparametric weighted stochastic block models](https://mlsanity.com/paper/1708.01432/) (2017)
- [Subsampling bootstrap of count features of networks](https://mlsanity.com/paper/1312.2645/) (2013)
- [Adapting the Stochastic Block Model to Edge-Weighted Networks](https://mlsanity.com/paper/1305.5782/) (2013)
- [Bootstrapping Exchangeable Random Graphs](https://mlsanity.com/paper/1711.00813/) (2017)
- [Testing and Modeling Dependencies Between a Network and Nodal Attributes](https://mlsanity.com/paper/1306.4708/) (2013)
- [A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network](https://mlsanity.com/paper/1806.11220/) (2018)
- [Snowboot: Bootstrap Methods for Network Inference](https://mlsanity.com/paper/1902.09029/) (2019)
- [Weighted network estimation by the use of topological graph metrics](https://mlsanity.com/paper/1705.00892/) (2017)

## Abstract (arXiv)

Dense networks with weighted connections often exhibit a community like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node's community membership. We propose a new framework for generating and estimating dense weighted networks with potentially different connectivity patterns across different communities. The proposed model relies on a particular class of functions which map individual node characteristics to the edges connecting those nodes, allowing for flexibility while requiring a small number of parameters relative to the number of edges. By leveraging the estimation techniques, we also develop a bootstrap methodology for generating new networks on the same set of vertices, which may be useful in circumstances where multiple data sets cannot be collected. Performance of these methods are analyzed in theory, simulations, and real data.