CONTAIN: A Community-based Algorithm for Network Immunization
This addresses the problem of harmful content diffusion in social networks for network analysts, but it appears incremental as it builds on existing immunization methods.
The authors tackled the problem of immunizing social networks against harmful content spread by proposing CONTAIN, a community-based algorithm that detects spreaders and partitions networks for immunization. Experimental results on real-world datasets show CONTAIN outperforms state-of-the-art methods like NetShield and SparseShield by converging faster in fewer iterations.
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.