Exponential Degree Distribution Research Articles

The behavior of rich-club coefficient in scale-free networks

The rich-club phenomenon, which provides information about the association between nodes, is a useful method to study the hierarchy structure of networks. In this work, we explore the behavior of rich-club coefficient (RCC) in scale-free networks, and find that the degree-based RCC is a power function of degree centrality with power exponent β and the betweenness-based RCC is a linear function of betweenness centrality with slope θ. Moreover, we calculate the value of RCC in a BA network by deleting nodes and obtain a general expression for RCC as a function of node sequence. On this basis, the solution of RCC for centrality is also obtained, which shows that the curve of RCC is determined by the centrality distribution. In the numerical simulation, we observe: β and γ (the degree distribution exponent) increase together, θ increases with the average degree and decreases to convergence as γ increases.

Majority-vote model with collective influence of hierarchical structures

Majority-vote model is a commonly used model in the study of opinion dynamics. In the original majority-vote model, the influence of node is determined by their neighbors. But there are nodes with low degree surrounded by nodes with high degree so they also have a great influence on the evolution of opinions. Therefore, the influence of a node should not only be measured by neighbors but also be connected to itself directly. Thus, this paper adds collective influence with hierarchical structures into the majority-vote model and measures opinion weight of center node by degree of their neighbors on hierarchical structures surround it with the set distance. The collective influence parameters used in this paper are related to the value of collective influence mentioned above and normalized by the maximum value of all nodes in system. The opinions’ evolution of majority-vote model with collective influence is studied in ER random networks and scale-free networks with different degree distribution exponents by Monte Carlo simulations. It is found that all systems have order-to-disorder phase transitions with the increase of noise parameter. When the depth of hierarchical structure is not zero, the system with collective influence is much easier to turn to disordered states so their critical noise parameters of phase transition are smaller than those of 0-depth systems and original majority-vote model. The reason is that high degree nodes in original majority-vote model have high influence because they are connected to more neighbors and nodes’ influence is also directly determined by degree in 0-depth collective influence model. Furthermore, nodes’ collective influence parameters in the system will all decrease when hierarchical structure of nonzero depth is considered, only a small number of individuals have high influence parameters in the system and they will make the opinions of surrounding individuals follow theirs, so if the opinions of a few highly influential individuals are out of order, then the system will reach a state of disorder. Because of the above factors, the collective influence model of nonzero depth is much easier to disorder with the increase of noise parameter. Besides, the system proves to be easier to reach a disordered state with the increase of degree distribution exponents in scale-free networks because all nodes’ degree will be lower so that the system will be dominated by less nodes with high degree. This conclusion verifies that scale-free networks are more similar to ER random networks with the increase of degree distribution exponents. Finally, through the finite-size scaling method, it is found that the phase transition of the majority-vote model with collective influence of hierarchical structures belongs in the Ising model universal class, whether in ER random networks or in scale-free networks.

Average Controllability of Complex Networks With Exponential Degree Distribution

Average Controllability of Complex Networks With Exponential Degree Distribution

A network model of social contacts with small-world and scale-free features, tunable connectivity, and geographic restrictions.

Small-world networks and scale-free networks are well-known theoretical models within the realm of complex graphs. These models exhibit "low" average shortest-path length; however, key distinctions are observed in their degree distributions and average clustering coefficients: in small-world networks, the degree distribution is bell-shaped and the clustering is "high"; in scale-free networks, the degree distribution follows a power law and the clustering is "low". Here, a model for generating scale-free graphs with "high" clustering is numerically explored, since these features are concurrently identified in networks representing social interactions. In this model, the values of average degree and exponent of the power-law degree distribution are both adjustable, and spatial limitations in the creation of links are taken into account. Several topological metrics are calculated and compared for computer-generated graphs. Unexpectedly, the numerical experiments show that, by varying the model parameters, a transition from a power-law to a bell-shaped degree distribution can occur. Also, in these graphs, the degree distribution is most accurately characterized by a pure power-law for values of the exponent typically found in real-world networks.

Droplet finite-size scaling of the majority-vote model on scale-free networks.

We discuss the majority vote model coupled with scale-free networks and investigate its critical behavior. Previous studies point to a nonuniversal behavior of the majority vote model, where the critical exponents depend on the connectivity. At the same time, the effective dimension D_{eff} is unity for a degree distribution exponent 5/2<γ<7/2. We introduce a finite-size theory of the majority vote model for uncorrelated networks and present generalized scaling relations with good agreement with Monte Carlo simulation results. Our finite-size approach has two sources of size dependence: an external field representing the influence of the mass media on consensus formation and the scale-free network cutoff. The critical exponents are nonuniversal, dependent on the degree distribution exponent, precisely when 5/2<γ<7/2. For γ≥7/2, the model is in the same universality class as the majority vote model on Erdős-Rényi random graphs. However, for γ=7/2, the critical behavior includes additional logarithmic corrections.

Robustness analysis of electricity networks against failure or attack: The case of the Australian National Electricity Market (NEM)

This study explores network science algorithms for the robustness analysis of electricity networks. We first investigate the characteristics of key network models including random graphs, small-world, and scale-free networks. Then, various measures are explored for the robustness of such networks against failure or attack, utilizing topological features and percolation theory. Both weighted and unweighted scenarios are studied, with network voltage considered as the edge weight. For a case study, we investigate the network characteristics as well as the robustness of the Australian National Electricity Market (NEM) network on the basis of these models and theories.The NEM is the world's longest interconnected power system, with an end-to-end distance of over 5000 km between the state of Queensland in the north and the state of South Australia. Our data contains 2375 transmission lines and 1538 nodes as generators or large demand customers. Our study shows that the NEM as an unweighted network is a small-world network (with exponential degree distribution). However, as a weighted network (considering the voltage capacity of nodes), it has a scale-free topology (following a power-law degree distribution). Robustness analysis revealed that the NEM presents relatively stronger robustness when facing random errors than when facing intentional attacks to nodes with a high degree centrality. It also revealed the sensitivity of the scale-free network to deliberate attacks directed toward important “hubs” (interconnected nodes).

A general model of hierarchical fractal scale-free networks.

We propose a general model of unweighted and undirected networks having the scale-free property and fractal nature. Unlike the existing models of fractal scale-free networks (FSFNs), the present model can systematically and widely change the network structure. In this model, an FSFN is iteratively formed by replacing each edge in the previous generation network with a small graph called a generator. The choice of generators enables us to control the scale-free property, fractality, and other structural properties of hierarchical FSFNs. We calculate theoretically various characteristic quantities of networks, such as the exponent of the power-law degree distribution, fractal dimension, average clustering coefficient, global clustering coefficient, and joint probability describing the nearest-neighbor degree correlation. As an example of analyses of phenomena occurring on FSFNs, we also present the critical point and critical exponents of the bond-percolation transition on infinite FSFNs, which is related to the robustness of networks against edge removal. By comparing the percolation critical points of FSFNs whose structural properties are the same as each other except for the clustering nature, we clarify the effect of the clustering on the robustness of FSFNs. As demonstrated by this example, the present model makes it possible to elucidate how a specific structural property influences a phenomenon occurring on FSFNs by varying systematically the structures of FSFNs. Finally, we extend our model for deterministic FSFNs to a model of non-deterministic ones by introducing asymmetric generators and reexamine all characteristic quantities and the percolation problem for such non-deterministic FSFNs.

Large degrees in scale-free inhomogeneous random graphs

We consider a class of scale-free inhomogeneous random graphs, which includes some long-range percolation models. We study the maximum degree in such graphs in a growing observation window and show that its limiting distribution is Frechet. We achieve this by proving convergence of the underlying point process of the degrees to a certain Poisson process. Estimating the index of the power-law tail for the typical degree distribution is an important question in statistics. We prove consistency of the Hill estimator for the inverse of the tail exponent of the typical degree distribution.

Polygon-Based Hierarchical Planar Networks Based on Generalized Apollonian Construction

Experimentally observed complex networks are often scale-free, small-world and have an unexpectedly large number of small cycles. An Apollonian network is one notable example of a model network simultaneously having all three of these properties. This network is constructed by a deterministic procedure of consequentially splitting a triangle into smaller and smaller triangles. In this paper, a similar construction based on the consequential splitting of tetragons and other polygons with an even number of edges is presented. The suggested procedure is stochastic and results in the ensemble of planar scale-free graphs. In the limit of a large number of splittings, the degree distribution of the graph converges to a true power law with an exponent, which is smaller than three in the case of tetragons and larger than three for polygons with a larger number of edges. It is shown that it is possible to stochastically mix tetragon-based and hexagon-based constructions to obtain an ensemble of graphs with a tunable exponent of degree distribution. Other possible planar generalizations of the Apollonian procedure are also briefly discussed.

Degree dependent transmission rates in epidemic processes

The outcome of SIR epidemics with heterogeneous infective lifetimes, or heterogeneous susceptibilities, can be mapped onto a directed percolation process on the underlying contact network. In this paper we study SIR models where heterogeneity is a result of the degree dependence of disease transmission rates. We develop numerical methods to determine the epidemic threshold, the epidemic probability and epidemic size close to the threshold for configuration model contact networks with arbitrary degree distribution and an arbitrary matrix of transmission rates (dependent on transmitting and receiving node degree). For the special case of separable transmission rates we obtain analytical expressions for these quantities. We propose a categorization of spreading processes based on the ratio of the probability of an epidemic and the expected size of an epidemic, and demonstrate that this ratio has a complex dependence on the degree distribution and the degree-dependent transmission rates. For scale-free contact networks and transmission rates that are power functions of transmitting and receiving node degrees, the epidemic threshold may be finite even when the degree distribution powerlaw exponent is below γ < 3. We give an expression, in terms of the degree distribution and transmission rate exponents, for the limit at which the epidemic threshold vanishes. We find that the expected epidemic size and the probability of an epidemic may grow nonlinearly above the epidemic threshold, with exponents that depend not only on the degree distribution powerlaw exponent, but on the parameters of the transmission rate degree dependence functions, in contrast to ordinary directed percolation and previously studied variations of the SIR model.

Measure for characterizing heavy-tailed networks

Heavy-tailed networks are often characterized in the literature by their degree distribution's similarity to a power law. However, many heavy-tailed networks in real life do not have power-law degree distributions, and in many applications the scale-free nature of the network is irrelevant so long as the network possesses hubs. Here we present the Cooke-Nieboer index (CNI), a non-asymptotic measure of the heavy-tailedness of a network's degree distribution which does not presume a power-law form. The CNI is easy to calculate, and clearly distinguishes between networks with power-law, exponential, and symmetric degree distributions.

Maximum Likelihood Estimation of Power-law Degree Distributions via Friendship Paradox-based Sampling

This article considers the problem of estimating a power-law degree distribution of an undirected network using sampled data. Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e.g., linear regression on double-logarithmic axes and maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution. As a solution, we present a novel maximum likelihood estimation approach that exploits the friendship paradox to sample more efficiently from the tail of the degree distribution. We analytically show that the proposed method results in a smaller bias, variance and a Cramèr–Rao lower bound compared to the vanilla maximum likelihood estimate obtained with uniformly sampled nodes (which is the most commonly used method in literature). Detailed numerical and empirical results are presented to illustrate the performance of the proposed method under different conditions and how it compares with alternative methods. We also show that the proposed method and its desirable properties (i.e., smaller bias, variance, and Cramèr–Rao lower bound compared to vanilla method based on uniform samples) extend to parametric degree distributions other than the power-law such as exponential degree distributions as well. All the numerical and empirical results are reproducible and the code is publicly available on Github.

Enhanced robustness of single-layer networks with redundant dependencies.

Dependency links in single-layer networks offer a convenient way of modeling nonlocal percolation effects in networked systems where certain pairs of nodes are only able to function together. We study the percolation properties of the weak variant of this model: Nodes with dependency neighbors may continue to function if at least one of their dependency neighbors is active. We show that this relaxation of the dependency rule allows for more robust structures and a rich variety of critical phenomena, as percolation is not determined strictly by finite dependency clusters. We study Erdős-Rényi and random scale-free networks with an underlying Erdős-Rényi network of dependency links. We identify a special "cusp" point above which the system is always stable, irrespective of the density of dependency links. We find continuous and discontinuous hybrid percolation transitions, separated by a tricritical point for Erdős-Rényi networks. For scale-free networks with a finite degree cutoff we observe the appearance of a critical point and corresponding double transitions in a certain range of the degree distribution exponent. We show that at a special point in the parameter space, where the critical point emerges, the giant viable cluster has the unusual critical singularity S-S_{c}∝(p-p_{c})^{1/4}. We study the robustness of networks where connectivity degrees and dependency degrees are correlated and find that scale-free networks are able to retain their high resilience for strong enough positive correlation, i.e., when hubs are protected by greater redundancy.

Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs.

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.

Random growth networks with exponential degree distribution.

A great variety of complex networks can be well represented as random graphs with some constraints: for instance, a provided degree distribution, a smaller diameter, and a higher clustering coefficient. Among them, the degree distribution has attracted considerable attention from various science communities in the last few decades. In this paper, we focus mainly on a family of random graphs modeling complex networks that have an exponential degree distribution; i.e., P(k)∼ exp(αk), where k is the degree of a vertex, P(k) is a probability for choosing randomly a vertex with degree equal to k, and α is a constant. To do so, we first introduce two types of operations: type-A operation and type-B operation. By both the helpful operations, we propose an available algorithm A for a seminal model to construct exactly solvable random graphs, which are able to be extended to a graph space S(p,pc,t) with probability parameters p and pc satisfying p+pc=1. Based on the graph space S(p,pc,t), we discuss several topological structure properties of interest on each member N(p,pc,t) itself and find model N(p,pc,t) to exhibit the small-world property and assortative mixing. In addition, we also report a fact that in some cases, two arbitrarily chosen members might have completely different other topological properties, such as the total number of spanning trees, although they share a degree distribution in common. Extensive experimental simulations are in strong agreement with our analytical results.

Spectral density of dense random networks and the breakdown of the Wigner semicircle law

Although the spectra of random networks have been studied for a long time, the influence of network topology on the dense limit of network spectra remains poorly understood. By considering the configuration model of networks with four distinct degree distributions, we show that the spectral density of the adjacency matrices of dense random networks is determined by the strength of the degree fluctuations. In particular, the eigenvalue distribution of dense networks with an exponential degree distribution is governed by a simple equation, from which we uncover a logarithmic singularity in the spectral density. We also derive a relation between the fourth moment of the eigenvalue distribution and the variance of the degree distribution, which leads to a sufficient condition for the breakdown of the Wigner semicircle law for dense random networks. Based on the same relation, we propose a classification scheme of the distinct universal behaviours of the spectral density in the dense limit. Our theoretical findings should lead to important insights on the mean-field behaviour of models defined on graphs.

Spatiotemporal evolution of the international plastic resin trade network

Plastics are the paradigmatic material of the current era. Plastics’ trade articulates interest in trading partnerships and concerns about trade security. We focus on the International Plastic Resin Trade Network (IPRTN) and analyze its spatiotemporal evolution in 1988–2017 from global, regional, and national scales. As a profile of globalization, the network became increasingly interconnected under the combined effect of the involvement of more participating countries, the increase in the closeness of trade links, and the increase in trade volume, which grew by 0.4-fold, 7.7-fold, and 14.9-fold, respectively. Despite the growth, IPRTN maintained fairly stable topological characteristics including small-world property (average path length, 1.95 ± 0.10; clustering coefficient, 0.63 ± 0.06), high reciprocity (reciprocity value, 0.54 ± 0.03), disassortative mixing (assortativity value, −0.46 ± 0.23), and exponential degree distribution. Generally, the plastic resin trade was spatially heterogeneous with high intra-regional trade proportions (1988–1997, 78.4%; 1998–2007, 79.9%; 2008–2017, 75.1%), and Europe, Asia, and North America were the dominating regions. These facts brought IPRTN with a regionally dependent community structure. Five communities were finally formed: the Middle East-Africa community, the Eastern Europe community, the Western Europe community, the Americas community, and the East Asia-Southeast Asia-Oceania community. We found that the US-China plastic resin trade was mainly complementary. Thus, mutual tariffs in the US-China trade war, which covers plastic resins, will cause adverse effects and need to be resolved through active consultations. To ensure trade security, we remind countries with poor trade robustness to pay attention to changes in trade policies of these critical trade players and to enrich trade channels.

FRACTAL NETWORKS ON SIERPINSKI-TYPE POLYGON

In this paper, the evolving networks are created from a series of Sierpinski-type polygon by applying the encoding method in fractal and symbolic dynamical system. Based on the self-similar structures of our networks, we study the cumulative degree distribution, the clustering coefficient and the standardized average path length. The power-law exponent of the cumulative degree distribution is deduced to be [Formula: see text] and the average clustering coefficients have a uniform lower bound [Formula: see text]. Moreover, we find the asymptotic formula of the average path length of our proposed networks. These results show the scale-free and the small-world effects of these networks.

Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality.

The healthcare resources supply network design for resilience is an effective way to deal with uncertainty disruption. In this article we propose a model of supply network self-organization evolution, and establish self-organized criticality as a cause of cascade failure. Our main purpose is to keep the system in a resilient range, i.e., critical state. A network structural design with smaller degree distribution exponent can achieve better absorptive capacity at macro level. An interactive rule design with extremal optimization has better adaptive capacity at micro level. Using macro statistic and indicator micro performance indicator, we demonstrate that our design can slow the development to a supercritical state and can improve the resilience of the supply network.

An Edge-Based Model of SEIR Epidemics on Static Random Networks.

Studies have been done using networks to represent the spread of infectious diseases in populations. For diseases with exposed individuals corresponding to a latent period, an SEIR model is formulated using an edge-based approach described by a probability generating function. The basic reproduction number is computed using the next generation matrix method and the final size of the epidemic is derived analytically. The SEIR model in this study is used to investigate the stochasticity of the SEIR dynamics. The stochastic simulations are performed applying continuous-time Gillespie's algorithm given Poisson and power law with exponential cut-off degree distributions. The resulting predictions of the SEIR model given the initial conditions match well with the stochastic simulations, validating the accuracy of the SEIR model. We varied the contribution of the disease parameters and the average degree of the network in order to investigate their effects on the spread of disease. We verified that the infection and the recovery rates show significant effects on the dynamics of the disease transmission. While the exposed rate delays the spread of the disease, increasing it towards infinity would lead to almost the same dynamics as that of an SIR case. A network with high average degree results to an early and higher peak of the epidemic compared to a network with low average degree. The results in this paper can be used as an alternative way of explaining the spread of disease and it provides implications on the control strategies applied to mitigate the disease transmission.