Multi-scale receptive fields: Graph attention neural network for hyperspectral image classification
Y Ding, Z Zhang, X Zhao, D Hong, W Cai… - Expert Systems with …, 2023 - Elsevier
Hyperspectral image (HSI) classification has attracted wide attention in many fields.
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …
Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification
Z Zhang, Y Ding, X Zhao, L Siye, N Yang, Y Cai… - Expert Systems with …, 2023 - Elsevier
In recent years, the applications of graph convolutional networks (GCNs) in hyperspectral
image (HSI) classification have attracted much attention. However, hyperspectral …
image (HSI) classification have attracted much attention. However, hyperspectral …
Learning knowledge graph embedding with a dual-attention embedding network
Abstract Knowledge Graph Embedding (KGE) aims to retain the intrinsic structural
information of knowledge graphs (KGs) via representation learning, which is critical for …
information of knowledge graphs (KGs) via representation learning, which is critical for …
Robust graph learning with graph convolutional network
Graph convolutional network (GCN) is a powerful tool to process the graph data and has
achieved satisfactory performance in the task of node classification. In general, GCN uses a …
achieved satisfactory performance in the task of node classification. In general, GCN uses a …
SGCN: A scalable graph convolutional network with graph-shaped kernels and multi-channels
Graph neural networks (GNNs) have demonstrated great success in graph processing.
However, current message-passing-based GNNs have limitations in terms of feature …
However, current message-passing-based GNNs have limitations in terms of feature …
{TYGR}: Type Inference on Stripped Binaries using Graph Neural Networks
Binary type inference is a core research challenge in binary program analysis and reverse
engineering. It concerns identifying the data types of registers and memory values in a …
engineering. It concerns identifying the data types of registers and memory values in a …
MC-GAT: multi-channel graph attention networks for capturing diverse information in complex graphs
Z La, Y Qian, H Leng, T Gu, W Gong, J Chen - Cognitive Computation, 2024 - Springer
Graph attention networks (GAT), which have strong performance in tackling various
analytical tasks on network data, have attracted wide attention. However, complex real-world …
analytical tasks on network data, have attracted wide attention. However, complex real-world …
Multi-strategy adaptive data augmentation for Graph Neural Networks
X Juan, X Liang, H Xue, X Wang - Expert Systems with Applications, 2024 - Elsevier
Abstract While existing Graph Neural Networks (GNNs) have demonstrated exceptional
performance in semi-supervised learning, they are criticized for not fully utilizing unlabeled …
performance in semi-supervised learning, they are criticized for not fully utilizing unlabeled …
Graph Convolutional Network Design for Node Classification Accuracy Improvement
Graph convolutional networks (GCNs) provide an advantage in node classification tasks for
graph-related data structures. In this paper, we propose a GCN model for enhancing the …
graph-related data structures. In this paper, we propose a GCN model for enhancing the …
[HTML][HTML] Probabilistic Graph Networks for Learning Physics Simulations
SKA Prakash, C Tucker - Journal of Computational Physics, 2024 - Elsevier
Inductive biases play a critical role in enabling Graph Networks (GN) to learn particle and
mesh-based physics simulations. In this paper, we propose two generalizable inductive …
mesh-based physics simulations. In this paper, we propose two generalizable inductive …