2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data
Jinghan Huang, Qiufeng Chen, Yijun Bian +4
Graph neural networks (GNNs) have proven effective in capturing relationships among nodes in a graph. This study introduces a novel perspective by considering a graph as a simplici…
cs.LG2024
Topological Cycle Graph Attention Network for Brain Functional Connectivity
Jinghan Huang, Nanguang Chen, Anqi Qiu
This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essenti…
cs.CV2023
Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data
Jinghan Huang, Moo K. Chung, Anqi Qiu
This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic…