ArticleSensors (Basel, Switzerland)2026
Key Node Identification in Spatial Information Networks via Soft Community Partition and Hypergraph Interweaving Degree.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Due to the widespread deployment of large-scale satellite constellations, Spatial Information Networks (SINs) exhibit increasingly high-order node and service coupling characteristics, rendering them vulnerable to node removal and cascading failures. Traditional graph-theoretic models fail to capture the inherent multi-node cooperative relationships within SINs, while conventional centrality metrics overlook service coupling features and cross-community propagation risks, leading to inaccurate key node identification. To address these issues, this paper proposes a key node mining method based on soft community partition and hypergraph interweaving degree. The method first constructs a hypernetwork to characterize the high-order associations among three types of services-communication, remote sensing, and navigation. Second, a probabilistic generative model, Hypergraph-MT, is introduced to detect overlapping communities and capture the multi-role attributes of satellites. Finally, the Interweaving Degree (IW) and its derived Bridge Score (IWBS) are defined to quantify node importance from two dimensions: load capacity and cross-community connectivity. Experimental results demonstrate that, compared with degree centrality and betweenness centrality, the proposed method increases the service interruption rate from 27.4% to 64.8% under a 5% removal ratio, and the service efficiency loss is elevated by more than 60%. In comparison with pure interweaving degree removal, IWBS triggers deeper cascading removal at a 30% removal ratio, reduces the removal cost by 8.9%, and improves the input-output ratio by 8.2% under high-intensity removal. Moreover, the algorithm exhibits linear scalability and strong robustness to parameter variations. The proposed method significantly enhances the accuracy and efficiency of key node identification in SINs, providing theoretical support for constellation deployment optimization and priority protection decision-making.
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