4.4 Article

Phase Transitions on Markovian Bipartite Graphs-an Application of the Zero-range Process

Journal

JOURNAL OF STATISTICAL PHYSICS
Volume 190, Issue SUPPL 1, Pages -

Publisher

SPRINGER
DOI: 10.1007/s10955-004-3011-z

Keywords

Random graph; giant component; zero-range process; condensation

Ask authors/readers for more resources

This article analyzes the existence and size of the giant component in a Markovian model for bipartite multigraphs, as well as its behavior at different parameter ranges. The specific example of Evans interaction is considered, and the impact of the critical exponent on the growth of the giant component is investigated.
We analyze the existence and the size of the giant component in the stationary state of a Markovian model for bipartite multigraphs, in which the movement of the edge ends on one set of vertices of the bipartite graph is a zero-range process, the degrees being static on the other set. The analysis is based on approximations by independent variables and on the results of Molloy and Reed for graphs with prescribed degree sequences. The possible types of phase diagrams are identified by studying the behavior below the zero-range condensation point. As a specific example, we consider the so-called Evans interaction. In particular, we examine the values of a critical exponent, describing the growth of the giant component as the value of the dilution parameter controlling the connectivity is increased above the critical threshold. Rigorous analysis spans a large portion of the parameter space of the model exactly at the point of zero-range condensation. These results, supplemented with conjectures supported by Monte Carlo simulations, suggest that the phenomenological Landau theory for percolation on graphs is not broken by the fluctuations.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available