scholarly journals On the pinning controllability of complex networks using perturbation theory of extreme singular values. application to synchronisation in power grids

2017 ◽  
Vol 7 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Stéphane Chrétien ◽  
◽  
Sébastien Darses ◽  
Christophe Guyeux ◽  
Paul Clarkson ◽  
...  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Franz Kaiser ◽  
Vito Latora ◽  
Dirk Witthaut

AbstractIn our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after. In this article, we introduce a general framework to analyse the spreading of failures in complex networks and demostrate that not only decreasing but also increasing the connectivity of the network can be an effective method to contain damages. We rigorously prove the existence of certain subgraphs, called network isolators, that can completely inhibit any failure spreading, and we show how to create such isolators in synthetic and real-world networks. The addition of selected links can thus prevent large scale outages as demonstrated for power transmission grids.


2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.


Energies ◽  
2015 ◽  
Vol 8 (9) ◽  
pp. 9211-9265 ◽  
Author(s):  
Lucas Cuadra ◽  
Sancho Salcedo-Sanz ◽  
Javier Del Ser ◽  
Silvia Jiménez-Fernández ◽  
Zong Geem

2014 ◽  
Vol 989-994 ◽  
pp. 4237-4240
Author(s):  
Zhi Kun Wang

If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.


Author(s):  
Antonio Scala ◽  
Guido Caldarelli ◽  
Alessandro Chessa ◽  
Alfonso Damiano ◽  
Mario Mureddu ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mehmet Emin Aktas ◽  
Thu Nguyen ◽  
Sidra Jawaid ◽  
Rakin Riza ◽  
Esra Akbas

AbstractDiffusion on networks is an important concept in network science observed in many situations such as information spreading and rumor controlling in social networks, disease contagion between individuals, and cascading failures in power grids. The critical interactions in networks play critical roles in diffusion and primarily affect network structure and functions. While interactions can occur between two nodes as pairwise interactions, i.e., edges, they can also occur between three or more nodes, which are described as higher-order interactions. This report presents a novel method to identify critical higher-order interactions in complex networks. We propose two new Laplacians to generalize standard graph centrality measures for higher-order interactions. We then compare the performances of the generalized centrality measures using the size of giant component and the Susceptible-Infected-Recovered (SIR) simulation model to show the effectiveness of using higher-order interactions. We further compare them with the first-order interactions (i.e., edges). Experimental results suggest that higher-order interactions play more critical roles than edges based on both the size of giant component and SIR, and the proposed methods are promising in identifying critical higher-order interactions.


2013 ◽  
Vol 392 (15) ◽  
pp. 3273-3280 ◽  
Author(s):  
Guidong Zhang ◽  
Zhong Li ◽  
Bo Zhang ◽  
Wolfgang A. Halang

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