# Compressing networks with super nodes

arXiv: https://arxiv.org/abs/1706.04110 · HTML: https://mlsanity.com/paper/1706.04110/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

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

- [SamBaS: Sampling-Based Stochastic Block Partitioning](https://mlsanity.com/paper/2108.06651/) (2021)
- [Community Concealment from Graph Neural Networks](https://mlsanity.com/paper/2602.12250/) (2026)

## Abstract (arXiv)

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network to be partitioned into a smaller network of 'super nodes', each super node comprising one or more nodes in the original network. To define the seeds of our super nodes, we apply the 'CoreHD' ranking from dismantling and decycling. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity, more stable across multiple (stochastic) runs within and between community detection algorithms, and overlap well with the results obtained using the full network.