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It is important for a networked-system to have both good controllability and robustness, where the former measures the ability that the networked system can be properly steered via control to any target state form any initial state within a finite time duration, while the later reflects how well this system can regain its controllability after being destructively attacked. Empirical observations suggest that multi-loop structures are beneficial for enhancing the controllability robustness. The Henneberg-growth mechanism in social networks offers a natural manner to generate multi-clique structures that can be developed to multi-loops by assigning proper edge directions. In this paper, a series of random polygon networks are generated, forming random triangle, rectangle, pentagon and hexagon networks. Then, their controllability robustness is investigated. A realistic measure is designed for characterizing their controllability robustness, which can be used to filter out trivial performances after the network is severely destructed, so that the computation and analysis become much more efficient. Extensive simulation results suggest that, for random polygon networks, 1) non-loop polygon structures are inferior to the loop polygons, confirming that the multi-loop structures are indeed beneficial for controllability robustness; 2) polygons with more sides possess better controllability robustness; and 3) the correlation between controllability robustness and connectivity robustness is weak, implying that the two objectives cannot be enhanced in the same way.
|Number of pages||12|
|Early online date||6 Jan 2022|
|Publication status||Published - Jan 2022|
Bibliographical noteThis research was supported in part by the National Natural Science Foundation of China (No. 62002249, 61873167), in part by the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P. R. China (No. Scip202103), in part by the Lam Woo Research Fund at Lingnan University (No. LWP20012), in part by the Research Committee of Lingnan University under Faculty Research Grant (No. DB22A3), and in part by the Hong Kong Research Grants Council under the GRF Grant CityU11206320.
- Complex network
- Linear systems
- Transmission line matrix methods
- Density measurement
- Social networking (online)
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- 1 Curtailed
A Convolutional Neural Network Approach for Adaptive Algorithm Selection
LOU, Y. F.
1/01/22 → 5/05/22
Project: Grant Research