The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A(k). When A(k) grows more slowly than linearly with k, the number of nodes with k links, N(k)(t), decays faster than a power law in k, while for A(k) growing faster than linearly ...

New entropy measures have been recently introduced for the quantification of the complexity of networks. Most of these entropy measures apply to static networks or to dynamical processes defined on static complex networks. In this paper we define the entropy rate of growing network models. This entropy rate quantifies how many labeled networks are typically generated by the growing network mode...

We study a social network consisting of over 10(4) individuals, with a degree distribution exhibiting two power scaling regimes separated by a critical degree k(crit), and a power law relation between degree and local clustering. We introduce a growing random model based on a local interaction mechanism that reproduces the observed scaling features and their exponents. We suggest that the doubl...

an equitable domination has interesting application in the contextof social networks. in a network, nodes with nearly equal capacitymay interact with each other in a better way. in the societypersons with nearly equal status, tend to be friendly. in thispaper, we introduce new variant of equitable domination of agraph. basic properties and some interesting results have beenobtained.

We introduce a stochastic model for complex networks possessing three qualitative features: power-law degree distributions, local clustering, and slowly growing diameter. The model is mathematically natural, permits a wide variety of explicit calculations, has the desired three qualitative features, and fits the complete range of degree scaling exponents and clustering parameters.

Approaches from statistical physics are applied to investigate the structure of network models whose growth rules mimic aspects of the evolution of the World Wide Web. We first determine the degree distribution of a growing network in which nodes are introduced one at a time and attach to an earlier node of degree k with rate Ak k. Very different behaviors arise for c < 1, c 1⁄4 1, and c > 1. W...