Social networks, characterized by community structures, often rely on nodes called structural hole spanners to facilitate inter-community information dissemination. However, the dynamic nature of these networks, where spanner nodes may be removed, necessitates resilient methods to maintain inter-community communication. To this end, we introduce robust spanners (RS) as nodes uniquely equipped to bridge communities despite disruptions, such as node or edge removals. We propose a novel scoring technique to identify RS nodes and present a parallel algorithm with a CUDA implementation for efficient RS detection in large networks. Empirical analysis of real-world social networks reveals that high-scoring nodes exhibit a spanning capacity comparable to those identified by benchmark spanner detection algorithms while offering superior robustness. Our implementation on Nvidia GPUs achieves an average speedup of 244X over traditional spanner detection techniques, demonstrating its efficacy to identify RS in large social networks.