90,179 research outputs found
Representation Learning for Scale-free Networks
Network embedding aims to learn the low-dimensional representations of
vertexes in a network, while structure and inherent properties of the network
is preserved. Existing network embedding works primarily focus on preserving
the microscopic structure, such as the first- and second-order proximity of
vertexes, while the macroscopic scale-free property is largely ignored.
Scale-free property depicts the fact that vertex degrees follow a heavy-tailed
distribution (i.e., only a few vertexes have high degrees) and is a critical
property of real-world networks, such as social networks. In this paper, we
study the problem of learning representations for scale-free networks. We first
theoretically analyze the difficulty of embedding and reconstructing a
scale-free network in the Euclidean space, by converting our problem to the
sphere packing problem. Then, we propose the "degree penalty" principle for
designing scale-free property preserving network embedding algorithm: punishing
the proximity between high-degree vertexes. We introduce two implementations of
our principle by utilizing the spectral techniques and a skip-gram model
respectively. Extensive experiments on six datasets show that our algorithms
are able to not only reconstruct heavy-tailed distributed degree distribution,
but also outperform state-of-the-art embedding models in various network mining
tasks, such as vertex classification and link prediction.Comment: 8 figures; accepted by AAAI 201
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RGFGA: An efficient representation and crossover for grouping genetic algorithms
There is substantial research into genetic algorithms that are used to group large numbers of
objects into mutually exclusive subsets based upon some fitness function. However, nearly all
methods involve degeneracy to some degree.
We introduce a new representation for grouping genetic algorithms, the restricted growth function
genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman
optimisation method and a well-established statistical clustering algorithm, with encouraging results
Analysis of approximate nearest neighbor searching with clustered point sets
We present an empirical analysis of data structures for approximate nearest
neighbor searching. We compare the well-known optimized kd-tree splitting
method against two alternative splitting methods. The first, called the
sliding-midpoint method, which attempts to balance the goals of producing
subdivision cells of bounded aspect ratio, while not producing any empty cells.
The second, called the minimum-ambiguity method is a query-based approach. In
addition to the data points, it is also given a training set of query points
for preprocessing. It employs a simple greedy algorithm to select the splitting
plane that minimizes the average amount of ambiguity in the choice of the
nearest neighbor for the training points. We provide an empirical analysis
comparing these two methods against the optimized kd-tree construction for a
number of synthetically generated data and query sets. We demonstrate that for
clustered data and query sets, these algorithms can provide significant
improvements over the standard kd-tree construction for approximate nearest
neighbor searching.Comment: 20 pages, 8 figures. Presented at ALENEX '99, Baltimore, MD, Jan
15-16, 199
Origin of Scaling Behavior of Protein Packing Density: A Sequential Monte Carlo Study of Compact Long Chain Polymers
Single domain proteins are thought to be tightly packed. The introduction of
voids by mutations is often regarded as destabilizing. In this study we show
that packing density for single domain proteins decreases with chain length. We
find that the radius of gyration provides poor description of protein packing
but the alpha contact number we introduce here characterize proteins well. We
further demonstrate that protein-like scaling relationship between packing
density and chain length is observed in off-lattice self-avoiding walks. A key
problem in studying compact chain polymer is the attrition problem: It is
difficult to generate independent samples of compact long self-avoiding walks.
We develop an algorithm based on the framework of sequential Monte Carlo and
succeed in generating populations of compact long chain off-lattice polymers up
to length . Results based on analysis of these chain polymers suggest
that maintaining high packing density is only characteristic of short chain
proteins. We found that the scaling behavior of packing density with chain
length of proteins is a generic feature of random polymers satisfying loose
constraint in compactness. We conclude that proteins are not optimized by
evolution to eliminate packing voids.Comment: 9 pages, 10 figures. Accepted by J. Chem. Phy
Multifractal Analysis of Packed Swiss Cheese Cosmologies
The multifractal spectrum of various three-dimensional representations of
Packed Swiss Cheese cosmologies in open, closed, and flat spaces are measured,
and it is determined that the curvature of the space does not alter the
associated fractal structure. These results are compared to observational data
and simulated models of large scale galaxy clustering, to assess the viability
of the PSC as a candidate for such structure formation. It is found that the
PSC dimension spectra do not match those of observation, and possible solutions
to this discrepancy are offered, including accounting for potential luminosity
biasing effects. Various random and uniform sets are also analyzed to provide
insight into the meaning of the multifractal spectrum as it relates to the
observed scaling behaviors.Comment: 3 latex files, 18 ps figure
Grain Dynamics in a Two-dimensional Granular Flow
We have used particle tracking methods to study the dynamics of individual
balls comprising a granular flow in a small-angle two-dimensional funnel. We
statistically analyze many ball trajectories to examine the mechanisms of shock
propagation. In particular, we study the creation of, and interactions between,
shock waves. We also investigate the role of granular temperature and draw
parallels to traffic flow dynamics.Comment: 17 pages, 24 figures. To appear in Phys.Rev.E. High res./color
figures etc. on http://www.nbi.dk/CATS/Granular/GrainDyn.htm
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