Effect of Size of Giant Component for actor node selection in WSANs: A comparison study

2019 ◽  
Vol 32 (8) ◽  
Author(s):  
Donald Elmazi ◽  
Miralda Cuka ◽  
Makoto Ikeda ◽  
Leonard Barolli
Keyword(s):  
Giant Component ◽  
Comparison Study ◽  
Node Selection ◽  
Size Of Giant Component

scholarly journals Identifying critical higher-order interactions in complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mehmet Emin Aktas ◽  
Thu Nguyen ◽  
Sidra Jawaid ◽  
Rakin Riza ◽  
Esra Akbas
Keyword(s):  
Complex Networks ◽  
Higher Order ◽  
Power Grids ◽  
Giant Component ◽  
Centrality Measures ◽  
First Order ◽  
Pairwise Interactions ◽  
Size Of Giant Component ◽  
Novel Method ◽  
Structure And Functions

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.

scholarly journals The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kai Gong ◽  
Yu Huang ◽  
Xiao-long Chen ◽  
Qing Li ◽  
Ming Tang
Keyword(s):  
The Self ◽  
Giant Component ◽  
Community Networks ◽  
Avalanche Breakdown ◽  
Self Awareness ◽  
Community Structures ◽  
Recovery Strategy ◽  
Recovery Model ◽  
Size Of Giant Component ◽  
Target Recovery

Many real infrastructure systems such as power grids and communication networks across cities not only depend on each other but also have community structures. This observation derives a new research subject of the interdependent community networks (ICNs). Recent works showed that the ICNs are extremely vulnerable to the failure of interconnected nodes between communities. Such vulnerability is prone to cause avalanche breakdown of the ICNs. How to improve the robustness of ICNs remains a challenge. In this paper, we propose a new target recovery strategy in the self-awareness recovery model, called recovery strategy based on community structures (RCS). The self-awareness recovery model repairs and reactivates the original pair of failed nodes that belong to mutual boundary of networks during cascading failures. The key insight is that the RCS explicitly considers both intercommunity links and intracommunity links. In this paper, we compare RCS with the state-of-the-art approaches based on randomness, degree centrality, and local centrality. We find that the RCS outperforms the other three strategies on the size of giant component, the existence probability of giant component, the number of iterative cascade steps, and the average degree of the remaining network. Moreover, RCS is robust against a given noise, and the optimal parameter of RCS remains stable even if the recovery ratio varies.

Implementation of intelligent fuzzy-based systems for actor node selection in WSANs: A comparison study considering effect of actor congestion situation

2018 ◽  
Vol 24 (3) ◽  
pp. 187-199 ◽  
Author(s):  
Donald Elmazi ◽  
Miralda Cuka ◽  
Kevin Bylykbashi ◽  
Evjola Spaho ◽  
Makoto Ikeda ◽  
...  
Keyword(s):  
Comparison Study ◽  
Node Selection

Performance Evaluation of WMN-GA Simulation System for Different Settings of Genetic Operators Considering Giant Component and Number of Covered Users

2012 ◽  
Vol 3 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Admir Barolli ◽  
Makoto Takizawa ◽  
Tetsuya Oda ◽  
Evjola Spaho ◽  
Leonard Barolli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Performance Evaluation ◽  
Mutation Rate ◽  
Simulation System ◽  
Giant Component ◽  
Genetic Operators ◽  
Crossover Rate ◽  
Size Of Giant Component ◽  
User Coverage ◽  
Simulation Results

In this paper, the authors propose and implement a system based on Genetic Algorithms (GAs) called WMN-GA. They evaluated the performance of WMN-GA for 0.7 crossover rate and 0.3 mutation rate, exponential ranking and different distribution of clients considering size of giant component and number of covered users parameters. The simulation results show that for normal distribution the system has better performance. The authors also carried out simulations for 0.8 crossover rate and 0.2 mutation rate. The simulation results show that the setting for 0.7 crossover rate and 0.3 mutation rate offers better connectivity and user coverage.

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