405 research outputs found
SURFACE INDUCED FINITE-SIZE EFFECTS FOR FIRST ORDER PHASE TRANSITIONS
We consider classical lattice models describing first-order phase
transitions, and study the finite-size scaling of the magnetization and
susceptibility. In order to model the effects of an actual surface in systems
like small magnetic clusters, we consider models with free boundary conditions.
For a field driven transition with two coexisting phases at the infinite volume
transition point , we prove that the low temperature finite volume
magnetization m_{\free}(L,h) per site in a cubic volume of size behaves
like
m_\free(L,h)=\frac{m_++m_-}2 + \frac{m_+-m_-}2
\tanh \bigl(\frac{m_+-m_-}2\,L^d\, (h-h_\chi(L))\bigr)+O(1/L),
where is the position of the maximum of the (finite volume)
susceptibility and are the infinite volume magnetizations at
and , respectively. We show that is shifted by an amount
proportional to with respect to the infinite volume transitions point
provided the surface free energies of the two phases at the transition
point are different. This should be compared with the shift for periodic boun\-
dary conditons, which for an asymmetric transition with two coexisting phases
is proportional only to . One also consider the position of
the maximum of the so called Binder cummulant U_\free(L,h). While it is again
shifted by an amount proportional to with respect to the infinite volume
transition point , its shift with respect to is of the much
smaller order . We give explicit formulas for the proportionality
factors, and show that, in the leading term, the relative shift is
the same as that for periodic boundary conditions.Comment: 65 pages, amstex, 1 PostScript figur
General Theory of Lee-Yang Zeros in Models with First-Order Phase Transitions
We present a general, rigorous theory of Lee-Yang zeros for models with
first-order phase transitions that admit convergent contour expansions. We
derive formulas for the positions and the density of the zeros. In particular,
we show that for models without symmetry, the curves on which the zeros lie are
generically not circles, and can have topologically nontrivial features, such
as bifurcation. Our results are illustrated in three models in a complex field:
the low-temperature Ising and Blume-Capel models, and the -state Potts model
for large enough.Comment: 4 pgs, 2 figs, to appear in Phys. Rev. Let
Scalable Methods for Adaptively Seeding a Social Network
In recent years, social networking platforms have developed into
extraordinary channels for spreading and consuming information. Along with the
rise of such infrastructure, there is continuous progress on techniques for
spreading information effectively through influential users. In many
applications, one is restricted to select influencers from a set of users who
engaged with the topic being promoted, and due to the structure of social
networks, these users often rank low in terms of their influence potential. An
alternative approach one can consider is an adaptive method which selects users
in a manner which targets their influential neighbors. The advantage of such an
approach is that it leverages the friendship paradox in social networks: while
users are often not influential, they often know someone who is.
Despite the various complexities in such optimization problems, we show that
scalable adaptive seeding is achievable. In particular, we develop algorithms
for linear influence models with provable approximation guarantees that can be
gracefully parallelized. To show the effectiveness of our methods we collected
data from various verticals social network users follow. For each vertical, we
collected data on the users who responded to a certain post as well as their
neighbors, and applied our methods on this data. Our experiments show that
adaptive seeding is scalable, and importantly, that it obtains dramatic
improvements over standard approaches of information dissemination.Comment: Full version of the paper appearing in WWW 201
Convergent Sequences of Dense Graphs I: Subgraph Frequencies, Metric Properties and Testing
We consider sequences of graphs and define various notions of convergence
related to these sequences: ``left convergence'' defined in terms of the
densities of homomorphisms from small graphs into the graphs of the sequence,
and ``right convergence'' defined in terms of the densities of homomorphisms
from the graphs of the sequence into small graphs; and convergence in a
suitably defined metric.
In Part I of this series, we show that left convergence is equivalent to
convergence in metric, both for simple graphs, and for graphs with nodeweights
and edgeweights. One of the main steps here is the introduction of a
cut-distance comparing graphs, not necessarily of the same size. We also show
how these notions of convergence provide natural formulations of Szemeredi
partitions, sampling and testing of large graphs.Comment: 57 pages. See also http://research.microsoft.com/~borgs/. This
version differs from an earlier version from May 2006 in the organization of
the sections, but is otherwise almost identica
Degree Distribution of Competition-Induced Preferential Attachment Graphs
We introduce a family of one-dimensional geometric growth models, constructed
iteratively by locally optimizing the tradeoffs between two competing metrics,
and show that this family is equivalent to a family of preferential attachment
random graph models with upper cutoffs. This is the first explanation of how
preferential attachment can arise from a more basic underlying mechanism of
local competition. We rigorously determine the degree distribution for the
family of random graph models, showing that it obeys a power law up to a finite
threshold and decays exponentially above this threshold.
We also rigorously analyze a generalized version of our graph process, with
two natural parameters, one corresponding to the cutoff and the other a
``fertility'' parameter. We prove that the general model has a power-law degree
distribution up to a cutoff, and establish monotonicity of the power as a
function of the two parameters. Limiting cases of the general model include the
standard preferential attachment model without cutoff and the uniform
attachment model.Comment: 24 pages, one figure. To appear in the journal: Combinatorics,
Probability and Computing. Note, this is a long version, with complete
proofs, of the paper "Competition-Induced Preferential Attachment"
(cond-mat/0402268
Approximating the partition function of the ferromagnetic Potts model
We provide evidence that it is computationally difficult to approximate the
partition function of the ferromagnetic q-state Potts model when q>2.
Specifically we show that the partition function is hard for the complexity
class #RHPi_1 under approximation-preserving reducibility. Thus, it is as hard
to approximate the partition function as it is to find approximate solutions to
a wide range of counting problems, including that of determining the number of
independent sets in a bipartite graph. Our proof exploits the first order phase
transition of the "random cluster" model, which is a probability distribution
on graphs that is closely related to the q-state Potts model.Comment: Minor correction
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