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• W1976578688 startingPage "P04011" @default.
• W1976578688 abstract "While a large number of methods for module detection have been developed for undirected networks, it is difficult to adapt them to handle directed networks due to the lack of consensus criteria for measuring the node significance in a directed network. In this paper, we propose a novel structural index, the control range, motivated by recent studies on the structural controllability of large-scale directed networks. The control range of a node quantifies the size of the subnetwork that the node can effectively control. A related index, called the control range similarity, is also introduced to measure the structural similarity between two nodes. When applying the index of control range to several real-world and synthetic directed networks, it is observed that the control range of the nodes is mainly influenced by the network's degree distribution and that nodes with a low degree may have a high control range. We use the index of control range similarity to detect and analyze functional modules in glossary networks and the enzyme-centric network of homo sapiens. Our results, as compared with other approaches to module detection such as modularity optimization algorithm, dynamic algorithm and clique percolation method, indicate that the proposed indices are effective and practical in depicting structural and modular characteristics of sparse directed networks." @default.
• W1976578688 created "2016-06-24" @default.
• W1976578688 creator A5027777470 @default.
• W1976578688 creator A5054607668 @default.
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• W1976578688 date "2012-04-20" @default.
• W1976578688 modified "2023-10-17" @default.
• W1976578688 title "Control range: a controllability-based index for node significance in directed networks" @default.
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• W1976578688 doi "https://doi.org/10.1088/1742-5468/2012/04/p04011" @default.
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