CONTAIN: A Community-based Algorithm for Network Immunization

Abstract

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.

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• Scikit-learn: Machine Learning in Python
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• It's All in the Embedding! Fake News Detection Using Document Embeddings
• MisRoBÆRTa: Transformers versus Misinformation
• MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media
• TextBenDS: a generic Textual data Benchmark for Distributed Systems

Cited by in corpus (1)

• MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media