Results 1 to 10 of about 5,573,586 (183)
On b-coloring of central graph of some graphs
Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 2018 Summary: The \(b\)-chromatic number of \(G\), denoted by \(\varphi(G)\), is the maximum \(k\) for which \(G\) has a \(b\)-coloring by \(k\) colors. A \(b\)-coloring of \(G\) by \(k\) colors is a proper \(k\)-coloring of the vertices of \(G\) such that in each color class \(i\) there exists a vertex \(x_i\) having neighbors in all the other \(k-1 ...
Kalpana, M., Vijayalakshmi, D.
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On central-peripheral appendage numbers of uniform central graphs [PDF]
Electronic Journal of Graph Theory and Applications, 2020 In a uniform central graph (UCG) the set of eccentric vertices of a central vertex is the same for all central vertices. This collection of eccentric vertices is the centered periphery.
Sul-Young Choi, Jonathan Needleman
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On the Total Graph of Mycielski Graphs, Central Graphs and Their Covering Numbers
Discussiones Mathematicae Graph Theory, 2013 The technique of counting cliques in networks is a natural problem. In this paper, we develop certain results on counting of triangles for the total graph of the Mycielski graph or central graph of star as well as completegraph families.
Patil H.P., Pandiya Raj R.
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A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2021 This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition. It is not only able to aggregate node information like a vanilla graph convolutional operation but also ...
Shuangyan Miao +4 more
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Rethinking the Expressive Power of GNNs via Graph Biconnectivity [PDF]
International Conference on Learning Representations, 2023 Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs in terms of the Weisfeiler-Lehman (WL) test, generally there is still a lack of deep ...
Bohang Zhang +3 more
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Energy-based Out-of-Distribution Detection for Graph Neural Networks [PDF]
International Conference on Learning Representations, 2023 Learning on graphs, where instance nodes are inter-connected, has become one of the central problems for deep learning, as relational structures are pervasive and induce data inter-dependence which hinders trivial adaptation of existing approaches that ...
Qitian Wu +3 more
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Commuting conjugacy classes graph of the generalized dihedral and dicyclic groups [PDF]
ریاضی و جامعه, 2023 Suppose $G$ is a finite non-abelian group and $\Gamma(G)$ is a simple graph with the non-central conjugacy classes of $G$ as its vertex set. Two different non-central conjugacy classes $A$ and $B$ are assumed to be adjacent if and only if there are ...
Mohammadali Salahshour
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Efficient generation of entangled multiphoton graph states from a single atom [PDF]
Nature, 2022 The central technological appeal of quantum science resides in exploiting quantum effects, such as entanglement, for a variety of applications, including computing, communication and sensing1.
P. Thomas, L. Ruscio, O. Morin, G. Rempe
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ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search [PDF]
Conference on Computer and Communications Security, 2023 We present ProvG-Searcher, a novel approach for detecting known APT behaviors within system security logs. Our approach leverages provenance graphs, a comprehensive graph representation of event logs, to capture and depict data provenance relations by ...
Enes Altinisik, Fatih Deniz, H. Sencar
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Research on Imbalance Fraud Detection Based on Graph Neural Network [PDF]
Jisuanji gongcheng, 2023 Currently, graph neural network is widely used in fraud detection. Because of the class imbalance problem in fraud detection, the performance of the model based on graph neural network is poor. To solve these problems, an unbalanced fraud detection model
Anqi CHEN, Rui CHEN, Zhufang KUANG, Huajun HUANG
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