scholarly journals How rare are power-law networks really?

2020 ◽  
Vol 476 (2241) ◽  
pp. 20190742
Author(s):  
I. Artico ◽  
I. Smolyarenko ◽  
V. Vinciotti ◽  
E. C. Wit
Keyword(s):  
Power Law ◽  
Ad Hoc ◽  
Null Distribution ◽  
Power Laws ◽  
Testing Procedure ◽  
Statistical Testing ◽  
Power Law Distribution ◽  
Scale Free ◽  
Common Structure ◽  
Power Of The Test

The putative scale-free nature of real-world networks has generated a lot of interest in the past 20 years: if networks from many different fields share a common structure, then perhaps this suggests some underlying ‘network law’. Testing the degree distribution of networks for power-law tails has been a topic of considerable discussion. Ad hoc statistical methodology has been used both to discredit power-laws as well as to support them. This paper proposes a statistical testing procedure that considers the complex issues in testing degree distributions in networks that result from observing a finite network, having dependent degree sequences and suffering from insufficient power. We focus on testing whether the tail of the empirical degrees behaves like the tail of a de Solla Price model, a two-parameter power-law distribution. We modify the well-known Kolmogorov–Smirnov test to achieve even sensitivity along the tail, considering the dependence between the empirical degrees under the null distribution, while guaranteeing sufficient power of the test. We apply the method to many empirical degree distributions. Our results show that power-law network degree distributions are not rare, classifying almost 65% of the tested networks as having a power-law tail with at least 80% power.

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.

Priority Weighted Fitness Model in Networks

2012 ◽  
Vol 229-231 ◽  
pp. 1854-1857
Author(s):  
Xin Yi Chen
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Common Property ◽  
World Wide ◽  
Genetic Networks ◽  
Power Law Distribution ◽  
Large Networks ◽  
Scale Free ◽  
The World ◽  
Complex Topology

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a power-law distribution. This feature was found to be a consequence of three generic mechanisms: (i) networks expand continuously by the addition of new vertices, (ii) new vertex with priority selected different edges of weighted selected that connected to different vertices in the system, and (iii) by the fitness probability that a new vertices attach preferentially to sites that are already well connected. A model based on these ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena. Experiment results show that the model is more close to the actual Internet network.

scholarly journals SYNCHRONIZABILITY AND SYNCHRONIZATION DYNAMICS OF WEIGHED AND UNWEIGHED SCALE FREE NETWORKS WITH DEGREE MIXING

2007 ◽  
Vol 17 (07) ◽  
pp. 2419-2434 ◽  
Author(s):  
FRANCESCO SORRENTINO ◽  
MARIO DI BERNARDO ◽  
FRANCO GAROFALO
Keyword(s):  
Power Law ◽  
Biological Networks ◽  
Optimization Strategy ◽  
Power Law Distribution ◽  
Scale Free ◽  
Degree Correlation ◽  
Topological Features ◽  
Low Degree ◽  
Correlation Properties ◽  
Law Degree

We study the synchronizability and the synchronization dynamics of networks of nonlinear oscillators. We investigate how the synchronization of the network is influenced by some of its topological features such as variations of the power law degree distribution exponent γ and the degree correlation coefficient r. Using an appropriate construction algorithm based on clustering the network vertices in p classes according to their degrees, we construct networks with an assigned power law distribution but changing degree correlation properties. We find that the network synchronizability improves when the network becomes disassortative, i.e. when nodes with low degree are more likely to be connected to nodes with higher degree. We consider the case of both weighed and unweighed networks. The analytical results reported in the paper are then confirmed by a set of numerical observations obtained on weighed and unweighed networks of nonlinear Rössler oscillators. Using a nonlinear optimization strategy we also show that negative degree correlation is an emerging property of networks when synchronizability is to be optimized. This suggests that negative degree correlation observed experimentally in a number of physical and biological networks might be motivated by their need to synchronize better.

scholarly journals Ultra-small scale-free geometric networks

2006 ◽  
Vol 43 (3) ◽  
pp. 665-677 ◽  
Author(s):  
J. E. Yukich
Keyword(s):  
Long Range ◽  
Power Law ◽  
Degree Distribution ◽  
Small Scale ◽  
Graph Distance ◽  
Power Law Distribution ◽  
Scale Free

We consider a family of long-range percolation models (Gp)p>0on ℤdthat allow dependence between edges and have the following connectivity properties forp∈ (1/d, ∞): (i) the degree distribution of vertices inGphas a power-law distribution; (ii) the graph distance between pointsxandyis bounded by a multiple of logpdlogpd|x-y| with probability 1 -o(1); and (iii) an adversary can delete a relatively small number of nodes fromGp(ℤd∩ [0,n]d), resulting in two large, disconnected subgraphs.

A Small-World Model of Scale-Free Networks: Features and Verifications

2011 ◽  
Vol 50-51 ◽  
pp. 166-170 ◽  
Author(s):  
Wen Jun Xiao ◽  
Shi Zhong Jiang ◽  
Guan Rong Chen
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Degree Sequence ◽  
Small World ◽  
Static Condition ◽  
Vertex Degree ◽  
Power Law Distribution ◽  
Least Common Multiple ◽  
Scale Free ◽  
Vertex Degrees

It is now well known that many large-sized complex networks obey a scale-free power-law vertex-degree distribution. Here, we show that when the vertex degrees of a large-sized network follow a scale-free power-law distribution with exponent  2, the number of degree-1 vertices, if nonzero, is of order N and the average degree is of order lower than log N, where N is the size of the network. Furthermore, we show that the number of degree-1 vertices is divisible by the least common multiple of , , . . ., , and l is less than log N, where l = < is the vertex-degree sequence of the network. The method we developed here relies only on a static condition, which can be easily verified, and we have verified it by a large number of real complex networks.

scholarly journals Exponential and Power Law Distribution of Contact Duration in Urban Vehicular Ad Hoc Networks

2013 ◽  
Vol 20 (1) ◽  
pp. 110-113 ◽  
Author(s):  
Yong Li ◽  
Depeng Jin ◽  
Zhaocheng Wang ◽  
Lieguang Zeng ◽  
Sheng Chen
Keyword(s):  
Ad Hoc Networks ◽  
Power Law ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Power Law Distribution ◽  
Contact Duration ◽  
Hoc Networks

scholarly journals COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN IN PEARL RIVER DELTA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1445-1449
Author(s):  
Y. Zeng
Keyword(s):  
Power Law ◽  
Pearl River Delta ◽  
Pearl River ◽  
Network Node ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Network Nodes ◽  
Scale Free ◽  
Node Distribution ◽  
Free Network

Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.

scholarly journals Power Laws in Superspreading Events: Evidence from Coronavirus Outbreaks and Implications for SIR Models

2020 ◽  
Author(s):  
Masao Fukui ◽  
Chishio Furukawa
Keyword(s):  
Power Law ◽  
Average Rate ◽  
Herd Immunity ◽  
Power Laws ◽  
Infinite Variance ◽  
Finite Variance ◽  
Fat Tails ◽  
Infection Rates ◽  
Power Law Distribution ◽  
Large Populations

AbstractWhile they are rare, superspreading events (SSEs), wherein a few primary cases infect an extraordinarily large number of secondary cases, are recognized as a prominent determinant of aggregate infection rates (ℛ0). Existing stochastic SIR models incorporate SSEs by fitting distributions with thin tails, or finite variance, and therefore predicting almost deterministic epidemiological outcomes in large populations. This paper documents evidence from recent coronavirus outbreaks, including SARS, MERS, and COVID-19, that SSEs follow a power law distribution with fat tails, or infinite variance. We then extend an otherwise standard SIR model with the estimated power law distributions, and show that idiosyncratic uncertainties in SSEs will lead to large aggregate uncertainties in infection dynamics, even with large populations. That is, the timing and magnitude of outbreaks will be unpredictable. While such uncertainties have social costs, we also find that they on average decrease the herd immunity thresholds and the cumulative infections because per-period infection rates have decreasing marginal effects. Our findings have implications for social distancing interventions: targeting SSEs reduces not only the average rate of infection (ℛ0) but also its uncertainty. To understand this effect, and to improve inference of the average reproduction numbers under fat tails, estimating the tail distribution of SSEs is vital.

Independence in time series: another look at the BDS test

1994 ◽  
Vol 348 (1688) ◽  
pp. 383-395 ◽  
Keyword(s):  
Time Series ◽  
Ad Hoc ◽  
Null Distribution ◽  
Spatial Arrangement ◽  
Testing Procedure ◽  
Correlation Integral ◽  
Geometrical Properties ◽  
The Central Limit Theorem ◽  
Original Time ◽  
Bds Test

This paper examines a statistical method derived from chaos theory. The correlation integral was proposed over a decade ago as a way of detecting chaos in a possibly partial realization of a dynamical system, because it depends on the spatial arrangement of the reconstructed attractor of the system. We exploit geometrical properties of an embedded time series to establish a test of independence in the original time series. Earlier efforts here have used the Central Limit Theorem to obtain normality as the null distribution; however, the testing procedure was, to an extent, ad hoc . By making moderately weak assumptions about the marginal distribution of the given series, we obtain a Poisson law for the correlation integral under the null hypothesis of independence, and use nonparametric methods to specify the test precisely. We compare the size and power of the present test with its predecessor and with other non-parametric tests for serial dependence.

scholarly journals Ultra-small scale-free geometric networks

2006 ◽  
Vol 43 (03) ◽  
pp. 665-677
Author(s):  
J. E. Yukich
Keyword(s):  
Long Range ◽  
Power Law ◽  
Degree Distribution ◽  
Small Scale ◽  
Graph Distance ◽  
Power Law Distribution ◽  
Scale Free

We consider a family of long-range percolation models (G p ) p&gt;0 on ℤ d that allow dependence between edges and have the following connectivity properties for p ∈ (1/d, ∞): (i) the degree distribution of vertices in G p has a power-law distribution; (ii) the graph distance between points x and y is bounded by a multiple of log pd log pd | x - y | with probability 1 - o(1); and (iii) an adversary can delete a relatively small number of nodes from G p (ℤ d ∩ [0, n] d ), resulting in two large, disconnected subgraphs.

Sign in / Sign up

Export Citation Format

Share Document