44 citations · 73 across the 18 of their papers we have counts for
6 papers · 1 filter
Graph Representation Learning for Infrared and Visible Image Fusion
Jing Li, Lu Bai, Bin Yang +3
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 loc…
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
Ahmed Begga, Francisco Escolano, Miguel Angel Lozano +1
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…
Labeled Subgraph Entropy Kernel
Chengyu Sun, Xing Ai, Zhihong Zhang +1
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 da…
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
Lixin Cui, Ming Li, Yue Wang +2
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 str…
Quantum Graph Learning: Frontiers and Outlook
Shuo Yu, Ciyuan Peng, Yingbo Wang +3
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 t…
State of the Art and Potentialities of Graph-level Learning
Zhenyu Yang, Ge Zhang, Jia Wu +9
Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as inp…