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Relevant bibliographies by topics / Graphons de probabilités

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Journal articles

Academic literature on the topic 'Graphons de probabilités'

Author: Grafiati

Published: 14 December 2024

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Journal articles on the topic "Graphons de probabilités"

1

McMillan, Audra, and Adam Smith. "When is non-trivial estimation possible for graphons and stochastic block models?‡." Information and Inference: A Journal of the IMA 7, no. 2 (2017): 169–81. http://dx.doi.org/10.1093/imaiai/iax010.

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Abstract Block graphons (also called stochastic block models) are an important and widely studied class of models for random networks. We provide a lower bound on the accuracy of estimators for block graphons with a large number of blocks. We show that, given only the number $k$ of blocks and an upper bound $\rho$ on the values (connection probabilities) of the graphon, every estimator incurs error ${\it{\Omega}}\left(\min\left(\rho, \sqrt{\frac{\rho k^2}{n^2}}\right)\right)$ in the $\delta_2$ metric with constant probability for at least some graphons. In particular, our bound rules out any n

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2

HATAMI, HAMED, and SERGUEI NORINE. "The Entropy of Random-Free Graphons and Properties." Combinatorics, Probability and Computing 22, no. 4 (2013): 517–26. http://dx.doi.org/10.1017/s0963548313000175.

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Every graphon defines a random graph on any given number n of vertices. It was known that the graphon is random-free if and only if the entropy of this random graph is subquadratic. We prove that for random-free graphons, this entropy can grow as fast as any subquadratic function. However, if the graphon belongs to the closure of a random-free hereditary graph property, then the entropy is O(n log n). We also give a simple construction of a non-step-function random-free graphon for which this entropy is linear, refuting a conjecture of Janson.

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3

Backhausz, Ágnes, and Dávid Kunszenti-Kovács. "On the dense preferential attachment graph models and their graphon induced counterpart." Journal of Applied Probability 56, no. 2 (2019): 590–601. http://dx.doi.org/10.1017/jpr.2019.34.

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AbstractLetting ℳ denote the space of finite measures on ℕ, and μλ ∊ ℳ denote the Poisson distribution with parameter λ, the function W : [0, 1]2 → ℳ given by W(x, y) = μc log x log y is called the PAG graphon with density c. It is known that this is the limit, in the multigraph homomorphism sense, of the dense preferential attachment graph (PAG) model with edge density c. This graphon can then in turn be used to generate the so-called W-random graphs in a natural way, and similar constructions also work in the slightly more general context of the so-called PAGκ models. The aim of this paper i

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4

Kalampakas, Antonios. "Graph Automata and Graph Colorability." European Journal of Pure and Applied Mathematics 16, no. 1 (2023): 112–20. http://dx.doi.org/10.29020/nybg.ejpam.v16i1.4629.

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Automata recognizing graphs can be constructed by employing the algebraic structure of graphoids. For the construction of a graph automaton, the relations over the Kleene star of the state set must constitute a graphoid. Hence different kinds of graphoids produce graph automata with diverse operation and recognition capacity. In this paper we show that graph colorability is recognized by automata operating over the simplest possible abelian graphoid.

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5

Zhang, Yuan, Elizaveta Levina, and Ji Zhu. "Estimating network edge probabilities by neighbourhood smoothing." Biometrika 104, no. 4 (2017): 771–83. http://dx.doi.org/10.1093/biomet/asx042.

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Summary The estimation of probabilities of network edges from the observed adjacency matrix has important applications to the prediction of missing links and to network denoising. It is usually addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but this is ill-defined without strong assumptions on the network structure. Here we propose a novel computationally efficient method, based on neighbourhood smoothing, to estimate the expectation of the adjacency matrix directly, without making the structural assumptions that graphon estimation requires. T

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6

Shi, Tan, Qing Peng, Zhitong Bai, Fei Gao, and Igor Jovanovic. "Proton irradiation of graphene: insights from atomistic modeling." Nanoscale 11, no. 43 (2019): 20754–65. http://dx.doi.org/10.1039/c9nr06502d.

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7

Peng, Songang, Zhi Jin, Dayong Zhang, Jingyuan Shi, Yanhui Zhang, and Guanghui Yu. "Evidence of electric field-tunable tunneling probability in graphene and metal contact." Nanoscale 9, no. 27 (2017): 9520–28. http://dx.doi.org/10.1039/c7nr02502e.

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8

Yoder, J. W., E. Littell, and B. T. Williams. "Probability Graphics Support for Medical Reasoning." Methods of Information in Medicine 32, no. 03 (1993): 229–32. http://dx.doi.org/10.1055/s-0038-1634928.

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Abstract:Graphic displays of data from clinical observations and laboratory testing provide important support to the health practitioner in managing an increasing amount of complex information. A graphic display program is described that preserves much of the context of data that is important to their evaluation, and that maintains a sense of the strength of the signal when aberrant data are encountered.

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9

VANTAGGI, BARBARA. "CONDITIONAL INDEPENDENCE STRUCTURES AND GRAPHICAL MODELS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 05 (2003): 545–71. http://dx.doi.org/10.1142/s0218488503002326.

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In this paper we study conditional independence structures arising from conditional probabilities and lower conditional probabilities. Such models are based on notions of stochastic independence apt to manage also those situations where zero evaluations on possible events are present: this is particularly crucial for lower probability. The "graphoid" properties of such models are investigated, and the representation problem of conditional independence structures is dealt with by generalizing the wellknown classic separation criteria for undirected and directed acyclic graphs. Our graphical mod

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10

Raturi, Ashish, and Sudhanshu Choudhary. "Simulation Study on Understanding the Spin Transport in MgO Adsorbed Graphene Based Magnetic Tunnel Junction." SPIN 06, no. 03 (2016): 1650011. http://dx.doi.org/10.1142/s2010324716500119.

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First principles calculations of spin-dependent electronic transport properties of magnetic tunnel junction (MTJ) consisting of MgO adsorbed graphene nanosheet sandwiched between two CrO2 half-metallic ferromagnetic (HMF) electrodes is reported. MgO adsorption on graphene opens bandgap in graphene nanosheet which makes it more suitable for use as a tunnel barrier in MTJs. It was found that MgO adsorption suppresses transmission probabilities for spin-down channel in case of parallel configuration (PC) and also suppresses transmission in antiparallel configuration (APC) for both spin-up and spi

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