scholarly journals An Approximation Algorithm for Average Path Length in A Small-World Network Model

2014 ◽  
Vol 03 (01) ◽  
pp. 22-28
Author(s):  
科 张
Keyword(s):  
Approximation Algorithm ◽  
Network Model ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Small World Network

A Kind of Deterministic Small-World Network Derived from Polygonal Nesting

2014 ◽  
Vol 614 ◽  
pp. 543-549
Author(s):  
Hui Li ◽  
Liang Yuan
Keyword(s):  
Long Range ◽  
Outer Layer ◽  
Degree Distribution ◽  
Path Length ◽  
Small Diameter ◽  
Small World ◽  
Average Path Length ◽  
Node Degree ◽  
Small World Network ◽  
Central Node

A kind of deterministic small-world network is constructed based on polygonal nesting with discrete degree distribution. By adding contrapuntal edges and alternate-position edges between adjacent nests, the intra-nest edges and the long-range edges from the central node to certain outer layer nodes, the proposed polygonal nesting small-world (PNSW) networks have the property of large clustering coefficients. Also these kinds of PNSW networks have small diameter, average node degree and average path length, whose moments ofkorder are given.

The Study on Wireless Sensor Network Topological Structure for Improving Network Energy Utilization

2013 ◽  
Vol 443 ◽  
pp. 509-512
Author(s):  
Yan Zhu ◽  
Yun Yu
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Complex Network ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Wireless Sensor ◽  
Small World Network

Based on the complex network property of wireless sensor networks, this study focus on the topology of wireless sensor network and carry out series simulation according to complex network research methods. The characteristic of topology for the wireless sensor network is probed in the experiment. The degree distribution, clustering coefficient and average path length are analyzed during the experimental process. Our results verify that the topology of wireless sensor network is neither regular nor random. It is between random network and small-world network which has comparatively smaller average path length and bigger cluster coefficient. In order to form a network that is similar to the property of small-world network, which can reduce network energy consumption by decrease the average hops, our experiment constructs a network model which has significant small-world network characteristic. The results indicate that the added small sum of long range edges in the network will not increase the network load while it can reduce the energy consumption of the network effectively.

scholarly journals Synchronizability of Small-World Networks Generated from a Two-Dimensional Kleinberg Model

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Yi Zhao ◽  
Jianwen Feng ◽  
Jingyi Wang
Keyword(s):  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Small World Network ◽  
Two Dimensional ◽  
Small World Networks

This paper investigates the synchronizability of small-world networks generated from a two-dimensional Kleinberg model, which is more general than NW small-world network. The three parameters of the Kleinberg model, namely, the distance of neighbors, the number of edge-adding, and the edge-adding probability, are analyzed for their impacts on its synchronizability and average path length. It can be deduced that the synchronizability becomes stronger as the edge-adding probability increases, and the increasing edge-adding probability could make the average path length of the Kleinberg small-world network go smaller. Moreover, larger distance among neighbors and more edges to be added could play positive roles in enhancing the synchronizability of the Kleinberg model. The lorentz oscillators are employed to verify the conclusions numerically.

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

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this 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.

Abstract 17426: Pulmonary Vein Isolation ‘Rewires’ Electrical Communications to Enhance Small-world Network Topology During Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Hiroshi Ashikaga ◽  
Jonathan Chrispin ◽  
Degang Wu ◽  
Joshua Garland
Keyword(s):  
Atrial Fibrillation ◽  
Communication Network ◽  
Mutual Information ◽  
Path Length ◽  
Random Network ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Network ◽  
Z Score ◽  
Network Properties

Recent evidence suggests that pulmonary vein isolation (PVI) may perturb the electrophysiological substrate for maintenance of atrial fibrillation (AF). Our previous work indicates that information theory metrics can quantify electrical communications during arrhythmia. We hypothesized that PVI ‘rewires’ the electrical communication network during AF such that the topology exhibits higher levels of small-world network properties, with higher clustering coefficient and lower path length, than would be expected by chance. Thirteen consecutive patients (n=6 with prior PVI and n=7 without) underwent AF ablation using a 64-electrode basket catheter in the left atrium. Multielectrode recording was performed during AF for 60 seconds, followed by PVI. Mutual information was calculated from the time series between each pair of electrodes using the Kraskov-Stögbauer-Grassberger estimator. The all-to-all mutual information matrix (64x64; Figure, upper panels) was thresholded by the median and standard deviations of mutual information to build a binary adjacency matrix for electrical communication networks. The properties of small-world network ( swn ; ‘small-world-ness’) were quantified by the ratio of the observed average clustering coefficient to that of a random network over the ratio of the observed average path length to that of a random network. swn was expressed in normal Z standard deviation units. As the binarizing threshold increased, the Z-score of swn decreased (Figure, lower panel). However, the Z-score at each threshold value was consistently higher with prior PVI than those without (p<0.05). In conclusion, electrical communication network during AF with prior PVI is associated with higher levels of small-world network properties than those without. This finding supports the concept that PVI perturbs the underlying substrate. In addition, swn of electrical communication network may be a promising metric to quantify substrate modification.

Sign in / Sign up

Export Citation Format

Share Document