In complex networks it is common to model a network or generate a surrogate network based on the conservation of the network’s degree distribution. We provide an alternative network model based on the conservation of connection density within a set of nodes. This density is measure by the rich–club coefficient. We present a method to generate surrogates networks with a given rich–club coefficie...

We establish the vanishing viscosity limit of the Navier-Stokes equations to the isentropic Euler equations for one-dimensional compressible fluid flow. For the NavierStokes equations, there exist no natural invariant regions for the equations with the real physical viscosity term so that the uniform sup-norm of solutions with respect to the physical viscosity coefficient may not be directly co...

Since some realistic networks are influenced not only by increment behavior but also by tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and tunable clustering mechanism, is proposed and its properties are investigated, suc...

A frequency-factor based approach for stochastic simulation of bivariate gamma distribution is proposed. The approach involves generation of bivariate normal samples with a correlation coefficient consistent with the correlation coefficient of the corresponding bivariate gamma samples. Then the bivariate normal samples are transformed to bivariate gamma samples using the well-known general equa...

A major issue in biology is the understanding of the interactions between proteins. These interactions can be described by a network, where the proteins are modeled by nodes and the interactions by edges. The origin of these protein networks is not well understood yet. Here we present a novel two-step model, which generates clusters with the same topological properties as networks for protein-p...

This paper studies nonlinear cointegrating models with time-varying coefficients and multiple nonstationary regressors using classic kernel smoothing methods to estimate the coefficient functions. Extending earlier work on nonstationary kernel regression to take account of practical features of the data, we allow the regressors to be cointegrated and to embody a mixture of stochastic and determ...

Distributed systems desire to construct a random overlay graph for robustness, efficient information dissemination and load balancing. A random walk-based overlay construction is a promising alternative to generate an ideal random scale free overlay in distributed systems. However, a simple random walk-based overlay construction can be affected by node churn. Especially, the number of edges inc...

Random networks generators like Erdős-Rényi, Watts-Strogatz and Barabási-Albert models are used as models to study real-world networks. Let G(V,E1) and G (V,E2) be two such networks on the same vertex set V . This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V,E1 ∪ E2). ar X iv :1 21 2. 54 04 v2 [ ph ys ic s. so cph ] 1 9 Ju n 20 13 Edge Union of...

We propose a wide class of preferential attachment models of random graphs, generalizing previous approaches. Graphs described by these models obey the power-law degree distribution, with the exponent that can be controlled in the models. Moreover, clustering coefficient of these graphs can also be controlled. We propose a concrete flexible model from our class and provide an efficient algorith...

We introduce a minimal model of growing small-world network generated by attaching to edges. The produced network is a plane graph which exists in real-life world. We obtain the analytic results of degree distribution decaying exponentially with degree and average clustering coefficient C = 3 2 ln3− 1 ≈ 0.6479, which are in good agreement with the numerical simulations. We also prove that the i...