Cascading Failures and the Robustness of Cooperation in a Unified Scale-Free Network Model

2022 ◽  
pp. 365-376
Author(s):  
Mingxuan He ◽  
Matthew Gao ◽  
Yang Gao ◽  
Fernanda M. Eliott
Keyword(s):  
Network Model ◽  
Cascading Failures ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiuwen Fu ◽  
Yongsheng Yang ◽  
Haiqing Yao
Keyword(s):  
Random Network ◽  
Small World ◽  
Coupled Map Lattice ◽  
Wireless Sensor ◽  
Cascading Failures ◽  
Scale Free Network ◽  
Scale Free ◽  
Network Topologies ◽  
Free Network ◽  
Entire Network

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference R and coupling coefficient ε will increase the risks of cascading failures. Cascading threshold values Rc and εc exist, where cascading failures will spread to the entire network when R>Rc or ε>εc. When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree k can improve the network invulnerability.

The Barabási and Albert scale-free network model

2018 ◽  
Vol 35 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Lei Zhu ◽  
Lei Wang ◽  
Xiang Zheng ◽  
Yuzhang Xu
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Scale-Free Networks Based on the Value of Interest

2013 ◽  
Vol 753-755 ◽  
pp. 2959-2962
Author(s):  
Jun Tao Yang ◽  
Hui Wen Deng
Keyword(s):  
Network Model ◽  
Power Law ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Distribution Property ◽  
Scale Free Networks ◽  
Free Model ◽  
Free Network ◽  
Interest Model

Assigning the value of interest to each node in the network, we give a scale-free network model. The value of interest is related to the fitness and the degree of the node. Experimental results show that the interest model not only has the characteristics of the BA scale-free model but also has the characteristics of fitness model, and the network has a power-law distribution property.

scholarly journals Geography in a scale-free network model

2002 ◽  
Vol 66 (5) ◽  
Author(s):  
C. P. Warren ◽  
L. M. Sander ◽  
I. M. Sokolov
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals Analytic solution of a static scale-free network model

2005 ◽  
Vol 44 (2) ◽  
pp. 241-248 ◽  
Author(s):  
M. Catanzaro ◽  
R. Pastor-Satorras
Keyword(s):  
Network Model ◽  
Analytic Solution ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

A novel scale-free network model based on clique growth

2009 ◽  
Vol 16 (3) ◽  
pp. 474-477 ◽  
Author(s):  
Bo Wang ◽  
Xu-hua Yang ◽  
Wan-liang Wang
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Model Based ◽  
Free Network

THE RELEVANCE-STRENGTH IN A SCALE-FREE NETWORK

2008 ◽  
Vol 22 (31) ◽  
pp. 3053-3059 ◽  
Author(s):  
HYUN-JOO KIM
Keyword(s):  
Network Model ◽  
Shortest Path ◽  
Power Law ◽  
Path Length ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean

We introduce a new quantity, relevance-strength which describes the relevance of a node to the others in a scale-free network. We define a weight between two nodes i and j based on the shortest path length between them and the relevance-strength of a node is defined as the sum of the weights between it and others. For the Barabási and Albert model which is a well-known scale-free network model, we measure the relevance-strength of each node and study the correlations with other quantities, such as the degree, the mean degree of neighbors of a node, and the mean relevance-strength of neighbors. We find that the relevance-strength shows power law behaviors and the crossover behaviors for the degree and the mean relevance-strength of neighbors. Also, we study the scaling behaviors of the relevance-strength for various average relevance-strength for all nodes.

scholarly journals Evolution of egalitarian punishment

2011 ◽  
Vol 2 (2) ◽  
pp. 20-23 ◽  
Author(s):  
Kohei Tamura ◽  
Rieko C Morita ◽  
Yasuo Ihara
Keyword(s):  
Network Model ◽  
Population Model ◽  
Computational Experiments ◽  
Evolution Of Cooperation ◽  
Scale Free Network ◽  
Social Equality ◽  
Scale Free ◽  
Altruistic Punishment ◽  
Individual Based Models ◽  
Free Network

Punishment has been deemed as a key to solve the puzzle of the evolution of cooperation. Recent studies have suggested that altruistic punishment may be motivated by preference for social equality (egalitarian punishment). Here we construct individual-based models to investigate the effectiveness of egalitarian punishment in promoting cooperation. Based on computational experiments, we first show that egalitarian punishment is as effective as classic punishment, which directly observes others' strategies, in a meta-population model. We then use a scale-free network model to show that egalitarian punishment can be effective even when heterogeneity in the number of interactions among individuals is incorporated. Finally, we show that generosity in punishment can affect co-evolution of egalitarian punishment and cooperation.

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