582 research outputs found
Bootstrap percolation on a graph with random and local connections
Let be a superposition of the random graph and a
one-dimensional lattice: the vertices are set to be on a ring with fixed
edges between the consecutive vertices, and with random independent edges given
with probability between any pair of vertices. Bootstrap percolation on a
random graph is a process of spread of "activation" on a given realisation of
the graph with a given number of initially active nodes. At each step those
vertices which have not been active but have at least active
neighbours become active as well. We study the size of the final active set in
the limit when . The parameters of the model are , the
size of the initially active set and the probability of
the edges in the graph.
Bootstrap percolation process on was studied earlier. Here we show
that the addition of local connections to the graph leads to a
more narrow critical window for the phase transition, preserving however, the
critical scaling of parameters known for the model on . We discover a
range of parameters which yields percolation on but not on
.Comment: 38 pages, 2 figure
The Ising model on the random planar causal triangulation: bounds on the critical line and magnetization properties
We investigate a Gibbs (annealed) probability measure defined on Ising spin
configurations on causal triangulations of the plane. We study the region where
such measure can be defined and provide bounds on the boundary of this region
(critical line). We prove that for any finite random triangulation the
magnetization of the central spin is sensitive of the boundary conditions.
Furthermore, we show that in the infinite volume limit, the magnetization of
the central spin vanishes for values of the temperature high enough.Comment: 28 pages, 2 figures, 1 section adde
A dynamic network in a dynamic population: asymptotic properties
We derive asymptotic properties for a stochastic dynamic network model in a
stochastic dynamic population. In the model, nodes give birth to new nodes
until they die, each node being equipped with a social index given at birth.
During the life of a node it creates edges to other nodes, nodes with high
social index at higher rate, and edges disappear randomly in time. For this
model we derive criterion for when a giant connected component exists after the
process has evolved for a long period of time, assuming the node population
grows to infinity. We also obtain an explicit expression for the degree
correlation (of neighbouring nodes) which shows that is always
positive irrespective of parameter values in one of the two treated submodels,
and may be either positive or negative in the other model, depending on the
parameters
- …