Results 21 to 30 of about 2,393,702 (209)
Boletim da Sociedade Paranaense de Matemática, 2019 A graph labeling is an assignment of integers to the vertices, edges, or to both, and it is subject to certain conditions. In this paper, a new concept of graph labeling called binary operation labeling is introduced.
Manal Naji Al-Harere +1 more
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Ratio Mathematica, 2023 In prime labeling, vertices are labeled from 1 to n, with the condition that any two adjacent vertices have relatively prime labels. Coprime labeling maintains the same criterion as prime labeling with adjacent vertices using any set of distinct positive
Janani R, Ramachandran T
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Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift [PDF]
The Web Conference, 2022 Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful training with amounts of expensive and time-consuming labeled data.
Hongrui Liu +5 more
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On the edge irregularity strength of grid graphs
AKCE International Journal of Graphs and Combinatorics, 2020 For a simple graph G, a vertex labeling is called a vertex -labeling. For any edge in , its weight . If all the edge weights are distinct, then is called an edge irregular -labeling of .
I. Tarawneh, R. Hasni, A. Ahmad
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On the reflexive edge strength of the circulant graphs
AIMS Mathematics, 2021 A labeling of a graph is an assignment that carries some sets of graph elements into numbers (usually the non negative integers). The total k-labeling is an assignment fe from the edge set to the set {1,2,...,ke} and assignment fv from the vertex set to ...
Mohamed Basher
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Electronic Journal of Combinatorics, 2018 A graph labeling is an assignment of integers to the vertices or edges, or both, subject to certain conditions. Graph labelings were first introduced in the mid 1960s. In the intervening 50 years nearly 200 graph labelings techniques have been studied in
J. Gallian
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Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning [PDF]
International Joint Conference on Artificial Intelligence, 2021 Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive
Ming Jin +5 more
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Gaussian Tribonacci R-Graceful Labeling of Some Tree Related Graphs
Ratio Mathematica, 2022 Let r be any natural number. An injective function , where is the Gaussian Tribonacci number in the Gaussian Tribonacci sequence is said to be Gaussian Tribonacci r-graceful labeling if the induced edge labeling such that is bijective.
K Sunitha, M Sheriba
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L(2,1)—labeling of the bracelet graph
Journal of Hebei University of Science and Technology, 2018 In order to better study the channel assignment problem, a function from the vertex set to the set of all nonnegative integers is generated, that is the L(2,1)—labeling of a graph. Let the least label be zero, the L(2,1)—labeling number of a graph is the
Haiping LI, Ying YANG
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-labeling of supersubdivided connected graph plus an edge
AKCE International Journal of Graphs and Combinatorics, 2020 Rosa, in his classical paper (Rosa, 1967) introduced a hierarchical series of labelings called and labeling as a tool to settle Ringel’s Conjecture which states that if is any tree with edges then the complete graph can be decomposed into copies of ...
G. Sethuraman, M. Sujasree
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