831 research outputs found
An algorithm for counting circuits: application to real-world and random graphs
We introduce an algorithm which estimates the number of circuits in a graph
as a function of their length. This approach provides analytical results for
the typical entropy of circuits in sparse random graphs. When applied to
real-world networks, it allows to estimate exponentially large numbers of
circuits in polynomial time. We illustrate the method by studying a graph of
the Internet structure.Comment: 7 pages, 3 figures, minor corrections, accepted versio
Sudden emergence of q-regular subgraphs in random graphs
We investigate the computationally hard problem whether a random graph of
finite average vertex degree has an extensively large -regular subgraph,
i.e., a subgraph with all vertices having degree equal to . We reformulate
this problem as a constraint-satisfaction problem, and solve it using the
cavity method of statistical physics at zero temperature. For , we find
that the first large -regular subgraphs appear discontinuously at an average
vertex degree c_\reg{3} \simeq 3.3546 and contain immediately about 24% of
all vertices in the graph. This transition is extremely close to (but different
from) the well-known 3-core percolation point c_\cor{3} \simeq 3.3509. For
, the -regular subgraph percolation threshold is found to coincide with
that of the -core.Comment: 7 pages, 5 figure
Ising Model on Networks with an Arbitrary Distribution of Connections
We find the exact critical temperature of the nearest-neighbor
ferromagnetic Ising model on an `equilibrium' random graph with an arbitrary
degree distribution . We observe an anomalous behavior of the
magnetization, magnetic susceptibility and specific heat, when is
fat-tailed, or, loosely speaking, when the fourth moment of the distribution
diverges in infinite networks. When the second moment becomes divergent,
approaches infinity, the phase transition is of infinite order, and size effect
is anomalously strong.Comment: 5 page
Self-organization of collaboration networks
We study collaboration networks in terms of evolving, self-organizing
bipartite graph models. We propose a model of a growing network, which combines
preferential edge attachment with the bipartite structure, generic for
collaboration networks. The model depends exclusively on basic properties of
the network, such as the total number of collaborators and acts of
collaboration, the mean size of collaborations, etc. The simplest model defined
within this framework already allows us to describe many of the main
topological characteristics (degree distribution, clustering coefficient, etc.)
of one-mode projections of several real collaboration networks, without
parameter fitting. We explain the observed dependence of the local clustering
on degree and the degree--degree correlations in terms of the ``aging'' of
collaborators and their physical impossibility to participate in an unlimited
number of collaborations.Comment: 10 pages, 8 figure
Statistical Mechanics of maximal independent sets
The graph theoretic concept of maximal independent set arises in several
practical problems in computer science as well as in game theory. A maximal
independent set is defined by the set of occupied nodes that satisfy some
packing and covering constraints. It is known that finding minimum and
maximum-density maximal independent sets are hard optimization problems. In
this paper, we use cavity method of statistical physics and Monte Carlo
simulations to study the corresponding constraint satisfaction problem on
random graphs. We obtain the entropy of maximal independent sets within the
replica symmetric and one-step replica symmetry breaking frameworks, shedding
light on the metric structure of the landscape of solutions and suggesting a
class of possible algorithms. This is of particular relevance for the
application to the study of strategic interactions in social and economic
networks, where maximal independent sets correspond to pure Nash equilibria of
a graphical game of public goods allocation
Universality in percolation of arbitrary Uncorrelated Nested Subgraphs
The study of percolation in so-called {\em nested subgraphs} implies a
generalization of the concept of percolation since the results are not linked
to specific graph process. Here the behavior of such graphs at criticallity is
studied for the case where the nesting operation is performed in an
uncorrelated way. Specifically, I provide an analyitic derivation for the
percolation inequality showing that the cluster size distribution under a
generalized process of uncorrelated nesting at criticality follows a power law
with universal exponent . The relevance of the result comes from
the wide variety of processes responsible for the emergence of the giant
component that fall within the category of nesting operations, whose outcome is
a family of nested subgraphs.Comment: 5 pages, no figures. Mistakes found in early manuscript have been
remove
6-Deoxyhexoses froml-Rhamnose in the Search for Inducers of the Rhamnose Operon: Synergy of Chemistry and Biotechnology
In the search for alternative nonâmetabolizable inducers in the l ârhamnose promoter system, the synthesis of fifteen 6âdeoxyhexoses from l ârhamnose demonstrates the value of synergy between biotechnology and chemistry. The readily available 2,3âacetonide of rhamnonolactone allows inversion of configuration at C4 and/or C5 of rhamnose to give 6âdeoxyâd âallose, 6âdeoxyâd âgulose and 6âdeoxyâl âtalose. Highly crystalline 3,5âbenzylidene rhamnonolactone gives easy access to l âquinovose (6âdeoxyâl âglucose), l âolivose and rhamnose analogue with C2 azido, amino and acetamido substituents. Electrophilic fluorination of rhamnal gives a mixture of 2âdeoxyâ2âfluoroâl ârhamnose and 2âdeoxyâ2âfluoroâl âquinovose. Biotechnology provides access to 6âdeoxyâl âaltrose and 1âdeoxyâl âfructose
Finding long cycles in graphs
We analyze the problem of discovering long cycles inside a graph. We propose
and test two algorithms for this task. The first one is based on recent
advances in statistical mechanics and relies on a message passing procedure.
The second follows a more standard Monte Carlo Markov Chain strategy. Special
attention is devoted to Hamiltonian cycles of (non-regular) random graphs of
minimal connectivity equal to three
Network growth for enhanced natural selection
Natural selection and random drift are competing phenomena for explaining the
evolution of populations. Combining a highly fit mutant with a population
structure that improves the odds that the mutant spreads through the whole
population tips the balance in favor of natural selection. The probability that
the spread occurs, known as the fixation probability, depends heavily on how
the population is structured. Certain topologies, albeit highly artificially
contrived, have been shown to exist that favor fixation. We introduce a
randomized mechanism for network growth that is loosely inspired in some of
these topologies' key properties and demonstrate, through simulations, that it
is capable of giving rise to structured populations for which the fixation
probability significantly surpasses that of an unstructured population. This
discovery provides important support to the notion that natural selection can
be enhanced over random drift in naturally occurring population structures
On large deviation properties of Erdos-Renyi random graphs
We show that large deviation properties of Erd\"os-R\'enyi random graphs can
be derived from the free energy of the -state Potts model of statistical
mechanics. More precisely the Legendre transform of the Potts free energy with
respect to is related to the component generating function of the graph
ensemble. This generalizes the well-known mapping between typical properties of
random graphs and the limit of the Potts free energy. For
exponentially rare graphs we explicitly calculate the number of components, the
size of the giant component, the degree distributions inside and outside the
giant component, and the distribution of small component sizes. We also perform
numerical simulations which are in very good agreement with our analytical
work. Finally we demonstrate how the same results can be derived by studying
the evolution of random graphs under the insertion of new vertices and edges,
without recourse to the thermodynamics of the Potts model.Comment: 38 pages, 9 figures, Latex2e, corrected and extended version
including numerical simulation result
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