Spatial network disintegration based on ranking aggregation

Disintegrating harmful networks presents a significant challenge, especially in spatial networks where both topological and geospatial features must be considered. Existing methods that rely on a single metric often fail to capture the full complexity of such networks. To address these limitations, we propose a novel ranking aggregation-based algorithm for spatial network disintegration. Our approach integrates multiple region centrality metrics, providing a comprehensive evaluation of region importance. The algorithm operates in two stages: first, multiple rankings based on different centrality metrics are aggregated into a composite ranking to refine the candidate regions for disintegration. In the second stage, an exact target enumeration method is applied within this candidate set to determine the optimal combination of regions that maximizes disintegration impact. This interconnected approach effectively combines ranking aggregation with targeted enumeration to ensure both efficiency and accuracy. Extensive experiments are conducted on synthetic and real-world spatial networks of different network configurations. The results demonstrate that our method consistently achieves superior disintegration performance compared to traditional approaches, effectively addressing the challenges associated with spatial network disintegration. This study provides a contribution to understanding and improving spatial network disintegration strategies by leveraging a comprehensive, multi-criteria approach.