Network structural perturbation aims to protect privacy by systematically studying the properties of networks and finding ways to disturb network analysis tools. This prevents the desired or correct analysis results being obtained, which can potentially be used to control the diffusion of misinformation on social networks. Network structure provides an effective representation to characterize entities and the relations between them for online systems, including social network platforms, electronic commerce websites, and computer network systems.

Due to the large volume of network data generated from complex online systems, many network analysis methods have been developed, such as link prediction, community detection, influential node ranking, node classification, and graph summarization. As a result, network privacy can be greatly challenged as the analysis tools obtained from these studies become more sophisticated and widespread and it becomes increasingly possible to infer confidential information. This raises privacy and security-related concerns as our data may be valuable not only to enterprises and public entities but also to cybercriminals who are increasingly relying on such tools for malicious purposes. Therefore, it is of great importance to analyze network structure perturbation methods systematically to mitigate such threats. Furthermore, network structural perturbation can be used to control the spread of misinformation on social networks, thus protecting the security of cyberspace.

This Special Issue aims to provide both theoretical and empirical investigations into network structure perturbation and its application to controlling misinformation spreading. We encourage related original research from different disciplines ranging from computer science, mathematics, and sociology. Furthermore, high-quality review articles describing the current state-of-the-art are also welcome.

Potential topics include but are not limited to the following:

• Large-scale social/computer network analysis
• Multiplex/temporal/high-order network analysis
• Network structural perturbation for large-scale networks
• Network structural perturbation strategy for microstructures
• Network structural perturbation strategy for mesoscale structures
• Assessment of network structural perturbation
• Prediction network structures
• Network structural perturbation for controlling misinformation spreading
• Mathematical models for misinformation spreading