MeshShield: Counteractive network immunization via spectral graph analysis

The rapid adoption of social media platforms by the general public has enabled the unprecedented spread of harmful content, often under the guise of anonymity, causing significant risks to individuals and communities, especially to vulnerable groups such as children, women, immigrants, etc. To stop the spread of such content and protect these groups, network immunization approaches from the epidemics domain have been employed. These immunization strategies use global spectral features of the graph, which struggle to scale to large-scale graphs that model real-world social media interactions and often fail to respond quickly to dynamic and community-localized outbreaks. To address this shortcoming, in this work, we introduce MeshShield , a novel immunization strategy that leverages the sparsity and community structure of social networks to enable efficient mitigation of harmful content by directly targeting infectious nodes and their neighbors. MeshShield applies a divide-et-impera approach to dynamically extract and analyze localized subgraphs centered around detected infectious sources, i.e., communities. Once these subgraphs are extracted, MeshShield employs a novel degree-weighted vulnerability measure to calculate which nodes within the community to immunize first. MeshShield analyzes all the subgraphs using a highly parallelized, shared-memory approach, thus reducing computational overhead and achieving near real-time performance even on large-scale real-world networks. Compared to state-of-the-art algorithms, such as DAVA and CONTAIN, the experimental results on real-world Twitter data demonstrate that our proposed strategy manages to immunize the network much faster while scaling with the graph’s size.