Results 51 to 60 of about 2,393,702 (209)
Resist Label Noise with PGM for Graph Neural Networks [PDF]
arXiv, 2023 While robust graph neural networks (GNNs) have been widely studied for graph perturbation and attack, those for label noise have received significantly less attention. Most existing methods heavily rely on the label smoothness assumption to correct noisy labels, which adversely affects their performance on heterophilous graphs.
arxiv
Heinz Quarter Mean Labeling of Graphs
Ratio MathematicaMean labeling is one of the best-known labeling methods for graphs. Despite the large number of papers published on the subject of graph labeling, there are some particular formulas to be used by researchers to mean-label graphs.
Latha S, Sandhya SS
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GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks [PDF]
arXiv, 2021 Graph Neural Networks have shown excellent performance on semi-supervised classification tasks. However, they assume access to a graph that may not be often available in practice. In the absence of any graph, constructing k-Nearest Neighbor (kNN) graphs from the given data have shown to give improvements when used with GNNs over other semi-supervised ...
arxiv
A Novel Problem to Solve the Logically Labeling of Corona between Paths and Cycles
Journal of Mathematics, 2022 In this study, we propose a new kind of graph labeling which we call logic labeling and investigate the logically labeling of the corona between paths Pn and cycles Cn, namely, Pn⊙Cm.
Ashraf ELrokh +2 more
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On inefficiently connecting temporal networks [PDF]
arXiv, 2023 A temporal graph can be represented by a graph with an edge labelling, such that an edge is present in the network if and only if the edge is assigned the corresponding time label. A journey is a labelled path in a temporal graph such that labels on successive edges of the path are increasing, and if all vertices admit journeys to all other vertices ...
arxiv
GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification [PDF]
arXiv, 2020 Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such as news annotation and product recommendation.
arxiv
On Magic Distinct Labellings of Simple Graphs [PDF]
arXiv, 2021 A magic labelling of a graph $G$ with magic sum $s$ is a labelling of the edges of $G$ by nonnegative integers such that for each vertex $v\in V$, the sum of labels of all edges incident to $v$ is equal to the same number $s$. Stanley gave remarkable results on magic labellings, but the distinct labelling case is much more complicated.
arxiv
AKCE International Journal of Graphs and Combinatorics, 2018 A graph G = ( V , E ) , where | V | = n and | E | = m is said to be a distance magic graph if there exists a bijection from the vertex set V to the set { 1 , 2 , … , n } such that, ∑ v ∈ N ( u ) f ( v ) = k , for all u ∈ V , which is a constant and ...
Aloysius Godinho, T. Singh
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Zhejiang Daxue xuebao. Lixue ban, 2016 Let f:E(G)→Z+ be an edge-weighting (labeing) of a graph G. For each v∈V(G) ， if yields a proper coloring of the graph, then f is defined as a neighbour-distinguishing edge labeling of G. Let g: V(G) ∪ E(G)→Z+ be a total-weighting (labeing) of a graph G.
LYUDamei(吕大梅) +1 more
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Totally magic d-lucky number of graphs
Ratio Mathematica, 2022 In this paper we introduce a new labeling named as, totally magic d-lucky labeling, find the totally magic d-lucky number of some standard graphs like wheel, cycle, bigraph etc. and find the totally magic d-lucky number of some zero divisor graphs.
N Mohamed Rilwan, A Nilofer
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