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Semi-supervised classification by graph p-Laplacian convolutional networks
Information Sciences, 2021The graph convolutional networks (GCN) generalizes convolution neural networks into the graph with an arbitrary topology structure. Since the geodesic function in the null space of the graph Laplacian matrix is constant, graph Laplacian fails to preserve
Sichao Fu +4 more
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Product of complete, strong, null, and regular fuzzy graphs
AIP Conference Proceedings, 2020In this article, we extend the work of Dogra [1]. The extension work is related to types of fuzzy graphs that be used. The fuzzy graphs complete, strong, null, and regular and consider to homomorphic, box dot, and star products. The exciting result of the study is regular fuzzy graphs constructed from various products from fuzzy graphs.
Toto Nusantara, Yuliana Trisanti
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A graph theoretic approach for spectral null codes
2009 IEEE Information Theory Workshop, 2009A new graph theoretic approach for spectral null codes is presented. Using the concept of index-permutation graph model and distance metrics, certain properties of spectral null codes were confirmed and verified with graph theory.
Khmaies Ouahada, Hendrik C. Ferreira
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Null-Hypothesis Testing with Graphs
2016Because biological processes are full of variations, statistics will give no certainties only chances. What chances? Chances that hypotheses are true/untrue. What hypotheses? For example: 1. our mean effect is not different from a 0 effect, 2. it is really different from a 0 effect, 3. it is worse than a 0 effect,
Ton J. Cleophas, Aeilko H. Zwinderman
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Coupling from the past for the null recurrent Markov chain
The Annals of Applied Probability, 2022The Doeblin Graph of a countable state space Markov chain describes the joint pathwise evolutions of the Markov dynamics starting from all possible initial conditions, with two paths coalescing when they reach the same point of the state space at the ...
F. Baccelli +2 more
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ON THE NULL-SPACES OF BICYCLIC SINGULAR GRAPHS
Discrete Mathematics, Algorithms and Applications, 2011In [M. Nath and B. K. Sarma, On the null-spaces of unicyclic and acyclic graphs, Linear Algebra Appl.427 (2007) 42–54], Nath and Sarma gave an algorithm to find a basis for the null-space of a graph G when G is singular acyclic or unicyclic. In this paper, we find a basis for the null-space of G when G is a bicyclic singular graph.
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HesGCN: Hessian graph convolutional networks for semi-supervised classification
Information Sciences, 2020Manifold or local geometry of samples have been recognized as a powerful tool in machine learning areas, especially in the graph-based semi-supervised learning (GSSL) problems. Over recent decades, plenty of manifold assumption-based SSL algorithms (MSSL)
Sichao Fu +4 more
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Exploring the Embedding of the Extended Zero-Divisor Graph of Commutative Rings
AxiomsRc represents commutative rings that have unity elements. The collection of all zero-divisor elements in Rc are represented by D(Rc). We denote an extended zero-divisor graph by the notation ℸ′(Rc) of Rc. This graph has a set of vertices in D(Rc)*.
A. Khabyah, M. A. Ansari
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, 2020
Early fault detection and diagnosis plays an important role in reducing maintenance cost and ensuring reliability of rolling element bearings (REBs). Singular value decomposition (SVD) is considered as a promising method to achieve this end, but lacks of
Xin Wen, Guoliang Lu, Jie Liu, Peng Yan
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Early fault detection and diagnosis plays an important role in reducing maintenance cost and ensuring reliability of rolling element bearings (REBs). Singular value decomposition (SVD) is considered as a promising method to achieve this end, but lacks of
Xin Wen, Guoliang Lu, Jie Liu, Peng Yan
semanticscholar +1 more source
2016
In the last chapter, a qualitative comparison of various real-world structures with classic random graph models revealed that complex networks are non-random in many aspects. This chapter focuses on the question of how to quantify the statistical significance of an observed network structure with respect to a given random graph model.
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In the last chapter, a qualitative comparison of various real-world structures with classic random graph models revealed that complex networks are non-random in many aspects. This chapter focuses on the question of how to quantify the statistical significance of an observed network structure with respect to a given random graph model.
openaire +1 more source