Rate equation approach for correlations in growing network models

Physics – Condensed Matter – Statistical Mechanics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

10.1103/PhysRevE.71.036127

We propose a rate equation approach to compute two vertex correlations in scale-free growing network models based in the preferential attachment mechanism. The formalism, based on previous work of Szab\'o \textit{et al.} [Phys. Rev. E \textbf{67} 056102 (2002)] for the clustering spectrum, measuring three vertex correlations, is based on a rate equation in the continuous degree and time approximation for the average degree of the nearest neighbors of vertices of degree $k$, with an appropriate boundary condition. We study the properties of both two and three vertex correlations for linear preferential attachment models, and also for a model yielding a large clustering coefficient. The expressions obtained are checked by means of extensive numerical simulations. The rate equation proposed can be generalized to more sophisticated growing network models, and also extended to deal with related correlation measures. As an example, we consider the case of a recently proposed model of weighted networks, for which we are able to compute a weighted two vertex correlation function, taking into account the strength of the interactions between connected vertices.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Rate equation approach for correlations in growing network models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Rate equation approach for correlations in growing network models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Rate equation approach for correlations in growing network models will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-497887

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.