Growing small-world networks based on a modified BA model

Abstract

We propose a simple growing model for the evolution of small-world networks. It is introduced as a modified BA model in which all the edges connected to the new nodes are made locally to the creator and its nearest neighbors. It is found that this model can produce small-world networks with power-law degree distributions. Properties of our model, including the degree distribution, clustering, and the average path length are compared with that of the BA model. Since most real networks are both scale-free and small-world networks, our model may provide a satisfactory description for empirical characteristics of real networks.

4 pages, 4 figures

References in corpus (11)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Assortative mixing in networks
• Hierarchical organization of modularity in metabolic networks
• Hierarchical Organization in Complex Networks
• Clustering and preferential attachment in growing networks
• Growing Scale-Free Networks with Tunable Clustering
• Truncation of power law behavior in "scale-free" network models due to information filtering
• Scale-free download network for publications
• Preferential attachment with information filtering - node degree probability distribution properties
• Mechanism for linear preferential attachment in growing networks