Identifying Antifragile Communities in Complex Networks by Complexity Assessment

Detecting antifragile communities is crucial for understanding complex network structures and dynamics. Communities can be categorized as fragile, robust or antifragile based on their inherent properties. This paper proposes a theoretical framework to identify community states using complexity measures. Initially, single‐node and multi‐node elimination perturbations are applied to selected communities within networks. Subsequently, the community's entropy before and after each perturbation and the resulting complexity variations are computed. To validate this framework, five distinct synthetic and three real‐world complex networks, each spanning multiple snapshots, are analysed. The findings reveal that communities with an initial entropy exceeding 0.5 out of 1 demonstrate antifragility, while those with initial entropy below 0.5 exhibit fragility in response to perturbations. Communities that become more complex are considered antifragile, whereas those that experience a decrease in complexity are deemed fragile. Our analysis also reveals a positive correlation between antifragility and both network density and average degree.