scholarly journals Enhancing Controllability Robustness of q-Snapback Networks through Redirecting Edges

Research ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Yang Lou ◽  
Lin Wang ◽  
Guanrong Chen
Keyword(s):  
Numerical Simulations ◽  
Network Model ◽  
Nearest Neighbor ◽  
Small World ◽  
Small World Network ◽  
Edge Removal ◽  
Regular Network ◽  
Node Removal ◽  
Network Controllability

The well-known small-world network model was established by randomly rewiring edges, aiming to enhance the synchronizability of an undirected nearest-neighbor regular network. This paper demonstrates via extensive numerical simulations that randomly redirecting edges could enhance the robustness of the network controllability for directed snapback networks against both random and intentional node-removal and edge-removal attacks.

A NOVEL SMALL-WORLD NETWORK MODEL: KEEPING CONNECTIVITY WITHOUT ADDING EDGES

2008 ◽  
Vol 22 (29) ◽  
pp. 5229-5234 ◽  
Author(s):  
XUHUA YANG ◽  
BO WANG ◽  
WANLIANG WANG ◽  
YOUXIAN SUN
Keyword(s):  
Network Model ◽  
Regular Ring ◽  
Nearest Neighbor ◽  
Small World ◽  
Clustering Coefficient ◽  
The Novel ◽  
Small World Network ◽  
Characteristic Path Length ◽  
Large N ◽  
Novel Model

Considering the problems of potentially generating a disconnected network in the WS small-world network model [Watts and Strogatz, Nature393, 440 (1998)] and of adding edges in the NW small-world network model [Newman and Watts, Phys. Lett. A263, 341 (1999)], we propose a novel small-world network model. First, generate a regular ring lattice of N vertices. Second, randomly rewire each edge of the lattice with probability p. During the random rewiring procedure, keep the edges between the two nearest neighbor vertices, namely, always keep a connected ring. This model need not add edges and can maintain connectivity of the network at all times in the random rewiring procedure. Simulation results show that the novel model has the typical small-world properties which are small characteristic path length and high clustering coefficient. For large N, the model is approximately equal to the WS model. For large N and small p, the model is approximately equal to the WS model or the NW model.

Small World Network Model Validation. Case Study of Suartone Historical Fire in Corsica

2021 ◽  
pp. 1-16
Author(s):  
N. Hamamousse ◽  
A. Kaiss ◽  
F. Giroud ◽  
N. Bozabalian ◽  
J-P. Clerc ◽  
...  
Keyword(s):  
Model Validation ◽  
Network Model ◽  
Small World ◽  
Small World Network

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

Small-World-Network Model Based Routing Method for Wireless Sensor Networks

2016 ◽  
Vol E99.B (11) ◽  
pp. 2315-2322
Author(s):  
Nobuyoshi KOMURO ◽  
Sho MOTEGI ◽  
Kosuke SANADA ◽  
Jing MA ◽  
Zhetao LI ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Model ◽  
Small World ◽  
Wireless Sensor ◽  
Small World Network ◽  
Model Based ◽  
Routing Method

How to Quantify 'Small-World Networks'?

Fractals ◽  
1998 ◽  
Vol 06 (04) ◽  
pp. 301-303 ◽  
Author(s):  
Hanspeter Herzel
Keyword(s):  
Small World ◽  
Small World Network ◽  
Small Worlds ◽  
Small World Networks ◽  
Complete Regularity ◽  
Local Clustering ◽  
Regular Network ◽  
Analytical Expressions ◽  
Global Connection

Recently Watts and Strogatz emphasized the widespread relevance of 'small worlds' and studied numerically networks between complete regularity and complete randomness. In this letter, I derive simple analytical expressions which can reproduce the empirical observations. It is shown how a few random connections can turn a regular network into a 'small-world network' with a short global connection but persisting local clustering.

SYNCHRONIZATION IN SMALL-WORLD DYNAMICAL NETWORKS

2002 ◽  
Vol 12 (01) ◽  
pp. 187-192 ◽  
Author(s):  
XIAO FAN WANG ◽  
GUANRONG CHEN
Keyword(s):  
Dynamical Systems ◽  
Coupling Strength ◽  
Continuous Time ◽  
Nearest Neighbor ◽  
Small World ◽  
Small World Network ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Number Of Cells

We investigate synchronization in a network of continuous-time dynamical systems with small-world connections. The small-world network is obtained by randomly adding a small fraction of connection in an originally nearest-neighbor coupled network. We show that, for any given coupling strength and a sufficiently large number of cells, the small-world dynamical network will synchronize, even if the original nearest-neighbor coupled network cannot achieve synchronization under the same condition.

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