# Immunization of networks with non-overlapping community structure

arXiv: https://arxiv.org/abs/1806.05637 · HTML: https://mlsanity.com/paper/1806.05637/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Artificial Benchmark for Community Detection with Outliers (ABCD+o)](https://mlsanity.com/paper/2301.05749/) (2023)
- [Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness](https://mlsanity.com/paper/2304.12036/) (2023)
- [CONTAIN: A Community-based Algorithm for Network Immunization](https://mlsanity.com/paper/2303.01934/) (2023)
- [A Modular Framework for Centrality and Clustering in Complex Networks](https://mlsanity.com/paper/2111.11623/) (2021)
- [Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks](https://mlsanity.com/paper/2201.06877/) (2022)

## Abstract (arXiv)

Although community structure is ubiquitous in complex networks, few works exploit this topological property to control epidemics. In this work, devoted to networks with non-overlapping community structure (i.e, a node belongs to a single community), we propose and investigate three deterministic immunization strategies. In order to characterize the influence of a node, various pieces of information are used such as the number of communities that the node can reach in one hop, the nature of the links (intra community links, inter community links), the size of the communities, and the interconnection density between communities. Numerical simulations with the Susceptible-Infected-Removed (SIR) epidemiological model are conducted on both real-world and synthetic networks. Experimental results show that the proposed strategies are more effective than classical deterministic alternatives that are agnostic of the community structure. Additionally, they outperform stochastic and deterministic strategies designed for modular networks.