Zobrazeno 1 - 10
of 25 432
pro vyhledávání: '"Edwin R"'
Autor:
LOGAN, P. BRADLEY1 Bradlogan56@gmail.com
Publikováno v:
Choral Journal. Mar2020, Vol. 60 Issue 8, p57-67. 11p.
Publikováno v:
RES: Anthropology and Aesthetics, 2005 Oct 01(48), 193-206.
Externí odkaz:
https://www.jstor.org/stable/20167687
Autor:
Yu, Yuantian, van Dam, Edwin R.
We consider Hoffman's program about the limit points of the spectral radius of the Hermitian adjacency matrix of mixed graphs. In particular, we determine all mixed graphs without negative $4$-cycle whose spectral radius does not exceed $\sqrt{2+\sqr
Externí odkaz:
http://arxiv.org/abs/2406.18318
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, duri
Externí odkaz:
http://arxiv.org/abs/2405.14742
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from the node representations. In this paper, we propose a new hierarchical
Externí odkaz:
http://arxiv.org/abs/2405.10218
An association scheme is called amorphic if every possible fusion of relations gives rise to a fusion scheme. We call a pair of relations fusing if fusing that pair gives rise to a fusion scheme. We define the fusing-relations graph on the set of rel
Externí odkaz:
http://arxiv.org/abs/2404.00567
Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the fused result, and only several recent approaches have tried to combin
Externí odkaz:
http://arxiv.org/abs/2403.16227
Autor:
Xu, Zhuo, Cui, Lixin, Li, Ming, Wang, Yue, Lyu, Ziyu, Du, Hangyuan, Bai, Lu, Yu, Philip S., Hancock, Edwin R.
In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a f
Externí odkaz:
http://arxiv.org/abs/2403.16133
Autor:
Qian, Feifei, Cui, Lixin, Li, Ming, Wang, Yue, Du, Hangyuan, Xu, Lixiang, Bai, Lu, Yu, Philip S., Hancock, Edwin R.
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between
Externí odkaz:
http://arxiv.org/abs/2403.16130
Autor:
Zhang, Yuke, van Dam, Edwin R.
Let $\mathcal{G}=\{G_1,\ldots,G_n \}$ be a family of graphs of order $n$ with the same vertex set. A rainbow Hamiltonian cycle in $\mathcal{G}$ is a cycle that visits each vertex precisely once such that any two edges belong to different graphs of $\
Externí odkaz:
http://arxiv.org/abs/2401.17845