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pro vyhledávání: '"Hancock, Edwin R."'
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
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
Infrared and visible image fusion aims to extract complementary features to synthesize a single fused image. Many methods employ convolutional neural networks (CNNs) to extract local features due to its translation invariance and locality. However, C
Externí odkaz:
http://arxiv.org/abs/2311.00291
High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the existing HO-GNNs are hop-based,
Externí odkaz:
http://arxiv.org/abs/2306.16976
In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial data analysis. However, existing methods, especially entropy based graph
Externí odkaz:
http://arxiv.org/abs/2303.13543
In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph structure, and computing the Averaged Mixing Matrix (AMM) to describe how
Externí odkaz:
http://arxiv.org/abs/2303.03396
Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates the current advances in quantum graph learning (QGL) from three perspec
Externí odkaz:
http://arxiv.org/abs/2302.00892