# CONTAIN: A Community-based Algorithm for Network Immunization

arXiv: https://arxiv.org/abs/2303.01934 · HTML: https://mlsanity.com/paper/2303.01934/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 7 of 7)

- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [Immunization of networks with non-overlapping community structure](https://mlsanity.com/paper/1806.05637/) (2018)
- [TextBenDS: a generic Textual data Benchmark for Distributed Systems](https://mlsanity.com/paper/2108.05689/) (2021)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media](https://mlsanity.com/paper/2302.12190/) (2023)
- [Misinformation Mitigation under Differential Propagation Rates and Temporal Penalties](https://mlsanity.com/paper/2206.11419/) (2022)

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

- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms](https://mlsanity.com/paper/2411.06138/) (2024)

## Abstract (arXiv)

Network immunization is an automated task in the field of network analysis that involves protecting a network (modeled as a graph) from being infected by an undesired arbitrary diffusion. In this article, we consider the spread of harmful content in social networks, and we propose CONTAIN, a novel COmmuNiTy-based Algorithm for network ImmuNization. Our solution uses the network information to (1) detect harmful content spreaders, and (2) generate partitions and rank them for immunization using the subgraphs induced by each spreader, i.e., employing CONTAIN. The experimental results obtained on real-world datasets show that CONTAIN outperforms state-of-the-art solutions, i.e., NetShield and SparseShield, by immunizing the network in fewer iterations, thus, converging significantly faster than the state-of-the-art algorithms. We also compared our solution in terms of scalability with the state-of-the-art tree-based mitigation algorithm MCWDST, as well as with NetShield and SparseShield. We can conclude that our solution outperforms MCWDST and NetShield.