9,586 research outputs found
Mining Frequent Neighborhood Patterns in Large Labeled Graphs
Over the years, frequent subgraphs have been an important sort of targeted
patterns in the pattern mining literatures, where most works deal with
databases holding a number of graph transactions, e.g., chemical structures of
compounds. These methods rely heavily on the downward-closure property (DCP) of
the support measure to ensure an efficient pruning of the candidate patterns.
When switching to the emerging scenario of single-graph databases such as
Google Knowledge Graph and Facebook social graph, the traditional support
measure turns out to be trivial (either 0 or 1). However, to the best of our
knowledge, all attempts to redefine a single-graph support resulted in measures
that either lose DCP, or are no longer semantically intuitive.
This paper targets mining patterns in the single-graph setting. We resolve
the "DCP-intuitiveness" dilemma by shifting the mining target from frequent
subgraphs to frequent neighborhoods. A neighborhood is a specific topological
pattern where a vertex is embedded, and the pattern is frequent if it is shared
by a large portion (above a given threshold) of vertices. We show that the new
patterns not only maintain DCP, but also have equally significant semantics as
subgraph patterns. Experiments on real-life datasets display the feasibility of
our algorithms on relatively large graphs, as well as the capability of mining
interesting knowledge that is not discovered in prior works.Comment: 9 page
A SVD accelerated kernel-independent fast multipole method and its application to BEM
The kernel-independent fast multipole method (KIFMM) proposed in [1] is of
almost linear complexity. In the original KIFMM the time-consuming M2L
translations are accelerated by FFT. However, when more equivalent points are
used to achieve higher accuracy, the efficiency of the FFT approach tends to be
lower because more auxiliary volume grid points have to be added. In this
paper, all the translations of the KIFMM are accelerated by using the singular
value decomposition (SVD) based on the low-rank property of the translating
matrices. The acceleration of M2L is realized by first transforming the
associated translating matrices into more compact form, and then using low-rank
approximations. By using the transform matrices for M2L, the orders of the
translating matrices in upward and downward passes are also reduced. The
improved KIFMM is then applied to accelerate BEM. The performance of the
proposed algorithms are demonstrated by three examples. Numerical results show
that, compared with the original KIFMM, the present method can reduce about 40%
of the iterating time and 25% of the memory requirement.Comment: 19 pages, 4 figure
Image classification by visual bag-of-words refinement and reduction
This paper presents a new framework for visual bag-of-words (BOW) refinement
and reduction to overcome the drawbacks associated with the visual BOW model
which has been widely used for image classification. Although very influential
in the literature, the traditional visual BOW model has two distinct drawbacks.
Firstly, for efficiency purposes, the visual vocabulary is commonly constructed
by directly clustering the low-level visual feature vectors extracted from
local keypoints, without considering the high-level semantics of images. That
is, the visual BOW model still suffers from the semantic gap, and thus may lead
to significant performance degradation in more challenging tasks (e.g. social
image classification). Secondly, typically thousands of visual words are
generated to obtain better performance on a relatively large image dataset. Due
to such large vocabulary size, the subsequent image classification may take
sheer amount of time. To overcome the first drawback, we develop a graph-based
method for visual BOW refinement by exploiting the tags (easy to access
although noisy) of social images. More notably, for efficient image
classification, we further reduce the refined visual BOW model to a much
smaller size through semantic spectral clustering. Extensive experimental
results show the promising performance of the proposed framework for visual BOW
refinement and reduction
Transport Coefficients from Extremal Gauss-Bonnet Black Holes
We calculate the shear viscosity of strongly coupled field theories dual to
Gauss-Bonnet gravity at zero temperature with nonzero chemical potential. We
find that the ratio of the shear viscosity over the entropy density is
, which is in accordance with the zero temperature limit of the ratio
at nonzero temperatures. We also calculate the DC conductivity for this system
at zero temperature and find that the real part of the DC conductivity vanishes
up to a delta function, which is similar to the result in Einstein gravity. We
show that at zero temperature, we can still have the conclusion that the shear
viscosity is fully determined by the effective coupling of transverse gravitons
in a kind of theories that the effective action of transverse gravitons can be
written into a form of minimally coupled scalars with a deformed effective
coupling.Comment: 23 pages, no figure; v2, refs added; v3, more refs added; v4, version
to appear in JHE
Eight-potential-well order-disorder ferroelectric model and effects of random fields
An eight-potential-well order-disorder ferroelectric model was presented and
the phase transition was studied under the mean-field approximation. It was
shown that the two-body interactions are able to account for the first-order
and the second order phase transitions. With increasing the random fields in
the system, a first-order phase transition is transformed into a second-order
phase transition, and furthermore, a second-order phase transition is
inhibited.
However, proper random fields can promote the spontaneous appearance of a
first-order phase transition by increasing the overcooled temperature. The
connections of the model with relaxors were discussed.Comment: 8 pages, 5 figures. Submitted to Applied Physics Letter
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