Efficient community detection in disaster networks using spectral sparsification

Community detection plays a critical role in disaster recovery and pervasive computing, where identifying cohesive social groups enables more effective communication, coordination, and resource allocation. In mobile and resource-constrained environments such as emergency response systems or mobile opportunistic networks, community detection methods must balance accuracy with computational efficiency. In this work, we propose a novel approach that uncovers community structures from a sparse representation of the original graph, addressing the need for lightweight and scalable algorithms in pervasive and mobile systems. Specifically, we apply Spielman–Srivastava spectral sparsification as a preprocessing step to reduce the number of edges while preserving the key spectral properties that underpin community structure. We then apply a modularity-optimizing genetic algorithm on the sparsified graph. Our experiments, conducted on both synthetic benchmarks and real-world networks, demonstrate that the proposed method, namely SSGA, achieves competitive or superior accuracy compared to state-of-the-art baselines, even under aggressive sparsification. We also analyze the cumulative computational complexity of the approach and provide an optimized implementation based on truncated spectral decomposition and parallel genetic operations. The results confirm that SSGA is not only accurate and robust but also computationally efficient, making it particularly well-suited for pervasive and mobile scenarios where time, energy, and connectivity are limited.