4 research outputs found
Continuous Percolation Phase Transitions of Two-dimensional Lattice Networks under a Generalized Achlioptas Process
The percolation phase transitions of two-dimensional lattice networks under a
generalized Achlioptas process (GAP) are investigated. During the GAP, two
edges are chosen randomly from the lattice and the edge with minimum product of
the two connecting cluster sizes is taken as the next occupied bond with a
probability . At , the GAP becomes the random growth model and leads
to the minority product rule at . Using the finite-size scaling analysis,
we find that the percolation phase transitions of these systems with are always continuous and their critical exponents depend on .
Therefore, the universality class of the critical phenomena in two-dimensional
lattice networks under the GAP is related to the probability parameter in
addition.Comment: 7 pages, 14 figures, accepted for publication in Eur. Phys. J.
Properties of a random attachment growing network
In this study we introduce and analyze the statistical structural properties
of a model of growing networks which may be relevant to social networks. At
each step a new node is added which selects 'k' possible partners from the
existing network and joins them with probability delta by undirected edges. The
'activity' of the node ends here; it will get new partners only if it is
selected by a newcomer. The model produces an infinite-order phase transition
when a giant component appears at a specific value of delta, which depends on
k. The average component size is discontinuous at the transition. In contrast,
the network behaves significantly different for k=1. There is no giant
component formed for any delta and thus in this sense there is no phase
transition. However, the average component size diverges for delta greater or
equal than one half.Comment: LaTeX, 19 pages, 6 figures. Discussion section, comments, a new
figure and a new reference are added. Equations simplifie
Time series irreversibility: a visibility graph approach
We propose a method to measure real-valued time series irreversibility which
combines two differ- ent tools: the horizontal visibility algorithm and the
Kullback-Leibler divergence. This method maps a time series to a directed
network according to a geometric criterion. The degree of irreversibility of
the series is then estimated by the Kullback-Leibler divergence (i.e. the
distinguishability) between the in and out degree distributions of the
associated graph. The method is computationally effi- cient, does not require
any ad hoc symbolization process, and naturally takes into account multiple
scales. We find that the method correctly distinguishes between reversible and
irreversible station- ary time series, including analytical and numerical
studies of its performance for: (i) reversible stochastic processes
(uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic
pro- cesses (a discrete flashing ratchet in an asymmetric potential), (iii)
reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv)
dissipative chaotic maps in the presence of noise. Two alternative graph
functionals, the degree and the degree-degree distributions, can be used as the
Kullback-Leibler divergence argument. The former is simpler and more intuitive
and can be used as a benchmark, but in the case of an irreversible process with
null net current, the degree-degree distribution has to be considered to
identifiy the irreversible nature of the series.Comment: submitted for publicatio