Results 1 to 10 of about 1,291,628 (288)

EDGE IRREGULAR REFLEXIVE LABELING OF DUMBBELL GRAPH, CORONA OF OPEN LADDER, AND NULL GRAPH

open access: yesBarekeng
Graph  is a simple, connected, undirected graph with vertex set  and edge set . A graph  is called to have an edge irregular reflexive -labeling if its vertices can be labeled with even numbers from  until  and its edges can be labeled with ...
Thetania Miftakul Zalsa   +2 more
doaj   +3 more sources

Null Model-Based Data Augmentation for Graph Classification [PDF]

open access: yesIEEE Transactions on Network Science and Engineering, 2021
Graph classification is an important task widely applied to biochemistry, social networks, and other fields. Since it is a data-dependent problem, insufficient training data will deteriorate the performance of graph classification models. To address this
Zeyu Wang   +6 more
semanticscholar   +4 more sources

Edge irregular reflexive labeling on sun graph and corona of cycle and null graph with two vertices

open access: yesIndonesian Journal of Combinatorics, 2021
Let G(V,E) be a simple and connected graph which set of vertices is V and set of edges is E. Irregular reflexive k-labeling f on G(V,E) is assignment that carries the numbers of integer to elements of graph, such that the positive integer {1,2, 3,...,ke}
Irfan Setiawan, Diari Indriati
doaj   +2 more sources

Gallai-Edmonds decomposition of unicyclic graphs from null space [PDF]

open access: yesThe American Journal of Combinatorics, 2022
In this paper, we compute the Gallai-Edmonds decomposition of a unicyclic graph $G$ using linear algebraic tools. More precisely, the Gallai-Edmonds decomposition of $G$ is obtained from the null space associated with adjacency matrices of its subtrees.
Luiz Emilio Allem   +3 more
doaj   +4 more sources

GACP: graph neural networks with ARMA filters and a parallel CNN for hyperspectral image classification

open access: yesInternational Journal of Digital Earth, 2023
In recent years, the use of convolutional neural networks (CNNs) and graph neural networks (GNNs) to identify hyperspectral images (HSIs) has achieved excellent results, and such methods are widely used in agricultural remote sensing, geological ...
Jing Yang   +5 more
doaj   +2 more sources

On local antimagic chromatic numbers of circulant graphs join with null graphs or cycles

open access: yesProyecciones (Antofagasta), 2023
An edge labeling of a graph G = (V,E) is said to be local antimagic if there is a bijection f : E → {1,..., |E|} such that for any pair of adjacent vertices x and y, f +(x) ≠ f +(y), where the induced vertex label is f +(x) = 𝜮 f(e), with e ranging over ...
G. Lau   +3 more
semanticscholar   +3 more sources

Edge Irregular Reflexive Labeling of Ladder Graph Corona Null Graph Families

open access: yesEuropean Journal of Pure and Applied Mathematics
Given a graph $G(V,E)$ or simply written as $G$. The graph labeling  was first introduced in 1960, it is a function that mapping integers or labels to graph elements (vertices, edges, or both of them) which must satisfy some certain criteria.
D. Indriati   +6 more
semanticscholar   +2 more sources

The split-and-drift random graph, a null model for speciation [PDF]

open access: yesStochastic Processes and their Applications, 2017
We introduce a new random graph model motivated by biological questions relating to speciation. This random graph is defined as the stationary distribution of a Markov chain on the space of graphs on $\{1, \ldots, n\}$.
Franccois Bienvenu   +2 more
semanticscholar   +5 more sources

On Null Vertex in Bipolar Fuzzy Graphs

open access: yesInternational Journal of Analysis and Applications
We present a novel vertex in Bipolar fuzzy graph, null vertex, which is distinct from boundary vertex and interior vertex and also attempt a study on null vertex in bipolar fuzzy closed helm graph CHn.
M. Sunil, J. Kumar
semanticscholar   +2 more sources

Graph‐based spectrum sensing algorithm via nonlinear function regulation

open access: yesIET Radar, Sonar & Navigation
To solve the difficulties in threshold selection and poor performance under low signal‐to‐noise ratio (SNR) conditions in existing spectrum sensing algorithms, a graph‐based spectrum sensing algorithm using nonlinear function regulation was proposed. The
Shanshan Wu, Guobing Hu
doaj   +2 more sources

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