Примеры использования Random graphs на Английском языке и их переводы на Русский язык
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Thus appear random graphs H n, p.
Random graphs, models and generators of scale-free graphs. .
Key words: random relations, random graphs.
Generating large random graphs with properties of social networks and given structure of user communities.
Every bipartite graph is of class 1, and almost all random graphs are of class 1.
In random graphs, the algebraic connectivity decreases with the number of vertices, and increases with the average degree.
Random regular graphs form a special case, with properties that may differ from random graphs in general.
From a mathematical perspective, random graphs are used to answer questions about the properties of typical graphs. .
To predict such changes it is necessary to study the general properties of mathematical models of such networks which can be considered as random graphs.
Random graphs may be described simply by a probability distribution, or by a random process which generates them.
Gamarnik et al. use a linear time algorithm for solving the problem on trees to study the asymptotic number of edges that must be added for sparse random graphs to make them Hamiltonian.
The random graphs of Erdős and Renyi are among the most important subjects of combinatorics and its applications.
The majority of theoretical and applied problems in modern mathematical physics, machine learning, computer science, numerical mathematics,processing of large data sets are naturally connected with analysis of high dimensional random objects, such as random matrices and random graphs.
Random graphs were first defined by Paul Erdős and Alfréd Rényi in their 1959 paper"On Random Graphs" and independently by Gilbert in his paper"Random graphs. .
Paley graphs are quasi-random(Chung et al. 1989): the number of times each possible constant-order graph occurs as a subgraph of a Paley graph is(in thelimit for large q) the same as for random graphs, and large sets of vertices have approximately the same number of edges as they would in random graphs.
The theory of random graphs studies typical properties of random graphs, those that hold with high probability for graphs drawn from a particular distribution.
Computational biology simulation of dynamics of biomolecular systems; neural network models of information processing in the brain structures;growing random graphs and their application in mathematical neurobiology; study of models of voltage-gated ion channels in excitable biomembranes; software for studying cell metabolism; Mathematical models in biomechanics; numerical solution of problems of cardiophysics.
Purely random graphs, built according to the Erdős-Rényi(ER) model, exhibit a small average shortest path length(varying typically as the logarithm of the number of nodes) along with a small clustering coefficient.
For instance, in the average case for sparse bipartite random graphs, Bast et al.(2006)(improving a previous result of Motwani 1994) showed that with high probability all non-optimal matchings have augmenting paths of logarithmic length.
In contrast, for random graphs in the Erdős-Rényi model with edge probability 1/2, both the maximum clique and the maximum independent set are much smaller: their size is proportional to the logarithm of n{\displaystyle n}, rather than growing polynomially.
The network probability matrix models random graphs through edge probabilities, which represent the probability p i, j{\displaystyle p_{i, j}} that a given edge e i, j{\displaystyle e_{i, j}} exists for a specified time period.
Different random graph models produce different probability distributions on graphs. .
Another model, which generalizes Gilbert's random graph model, is the random dot-product model.
In mathematics, random graph is the general term to refer to probability distributions over graphs. .
Given a random graph of n{\displaystyle n} nodes and an average degree⟨ k⟩{\displaystyle\langle k\rangle.
Almost every sufficiently sparse random graph is pseudoforest.
Renyi initiated the study of the binomial model of a random graph G( n, p), in which edges on n vertices are drawn independently, each with probability p.
The existence of a property on a random graph can often imply, via the Szemerédi regularity lemma, the existence of that property on almost all graphs. .
Decomposition elements relation design is been modeled as connected random graph construction process by Erdosh-Renyi model.