ArticleScientific reports2025
Controllability preservation in complex networks via minimal edge configurations.
Article in Scientific reports, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
Funding
Abstract
Recent advances in networked control systems have resulted in linear time-invariant (LTI) networks of increasing scale and complexity, thereby presenting significant challenges for controllability optimization. This study proposes a novel optimization framework to enhance control efficiency and reduce network complexity in large-scale complex networks, leveraging exact controllability theory and matrix similarity transformations for both directed and undirected topologies. Firstly, a method is introduced for identifying node correspondences before and after similarity transformations of the network's adjacency matrix. Subsequently, a minimal-edge connection framework is proposed to link Jordan blocks associated with distinct eigenvalues in the Jordan canonical form. Finally, building upon this method and framework, a specific optimization strategy is developed to minimize the number of inter-node connections while preserving network controllability. The proposed approach effectively simplifies the network structure and improves control efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.