In order to take the weight of connection into consideration and to find a natural measurement of weight, we have collected papers in Econophysics and constructed a network of scientific communication to integrate idea transportation among econophysicists by collaboration, citation and personal...

In present paper, we propose a highly clustered weighted network model that incorporates the addition of a new node with some links, new links between existing nodes and the edge's weight dynamical evolution based on weight-dependent walks at each time step. The analytical approach and numerical...

A stochastic model for the evolution of a cellular network driven by dissipative forces is presented. The model is based on a variational formulation for the dissipated power, from which we obtain an expression for the transition-rate generating function to be used in kinetic Monte Carlo...

In this paper, we develop a general analytical method to compute clustering coefficients of growing networks. This method can be applied to any network as long as we can construct and solve the dynamic equation for the degree of any node. We also verify the accuracy of the method through simulation.

By incorporating local traffic information into the basic shortest path routing policy, we propose a congestion awareness routing strategy with a tunable parameter. We investigate the effectiveness of the proposed routing strategy for scale-free networks with different clustering coefficients...

We propose several characterizations of weighted complex networks by incorporating the concept of metaweight into the clustering coefficient, degree correlation, and module decomposition. These incorporations make it possible to describe weighted networks depending on how strongly we emphasize...

In recent years there has been considerable interest in the structure and dynamics of complex networks. One of the most studied networks is the linear Barabási–Albert model. Here we investigate the nonlinear Barabási–Albert growing network. In this model, a new node connects to a vertex of...

Most real-world networks exhibit a high clustering coefficient—the probability that two neighbors of a node are also neighbors of each other. We propose two algorithms, Conf and Throw, that take triangle and single edge degree sequences as input and generate a random graph with a target...

Using the network random generation models from Gustedt (2009) [23], we simulate and analyze several characteristics (such as the number of components, the degree distribution and the clustering coefficient) of the generated networks. This is done for a variety of distributions (fixed value,...

The cluster-degree of a vertex is the number of connections among the neighbors of this vertex. In this paper we study the cluster-degree of the generalized Barabási–Albert model (GBA model) whose exponent of degree distribution ranges from 2 to ∞. We present the mean-field rate equation...