There are many researchers proposed social network models in recent years, and most of them focus on clustering coefficient property of a small-world network and power law degree distribution of a scale-free property. In social network topology, we observed the network is consisted of many nodes with small connectivity and a few high-degree nodes. In the small connectivity part, there are many ...

Aiming to understand real-world hierarchical networks whose degree distributions are neither power law nor exponential, we construct a hybrid clique network that includes both homogeneous and inhomogeneous parts, and introduce an inhomogeneity parameter to tune the ratio between the homogeneous part and the inhomogeneous one. We perform Monte-Carlo simulations to study various properties of suc...

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...