# Epidemic spreading on complex networks with community structures

arXiv: https://arxiv.org/abs/1611.06092 · HTML: https://mlsanity.com/paper/1611.06092/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 4 of 4)

- [Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems](https://mlsanity.com/paper/2309.13229/) (2023)
- [Stationary distribution of node2vec random walks on household models](https://mlsanity.com/paper/2502.19039/) (2025)
- [An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs](https://mlsanity.com/paper/2112.10953/) (2021)
- [SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search](https://mlsanity.com/paper/2506.03657/) (2025)

## Abstract (arXiv)

Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both textitenforce as well as textitinhibit diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.