[HTML][HTML] Fusion-based graph neural networks for synergistic underwater image enhancement
Underwater images have become essential tools for marine exploration. However, their
quality is often diminished by specific phenomena inherent to aquatic environments, thereby …
quality is often diminished by specific phenomena inherent to aquatic environments, thereby …
Node classification based on structure migration and graph attention convolutional crossover network
R Li, C Wang, R Shang, W Zhang, S Xu - Knowledge-Based Systems, 2025 - Elsevier
Due to the sparse structure of graph and GCN (Graph Convolutional Networks) does not
consider neighbor node specificity, graph nodes are over-smoothed after passing through …
consider neighbor node specificity, graph nodes are over-smoothed after passing through …
Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks
Identifying links within biological networks is important in various biomedical applications.
Recent studies have revealed that each node in a network may play a unique role in …
Recent studies have revealed that each node in a network may play a unique role in …
[PDF][PDF] Word distance assisted dual graph convolutional networks for accurate and fast aspect-level sentiment analysis
J Jiao, H Wang, R Shen, Z Lu - Mathematical Biosciences and …, 2024 - aimspress.com
Aspect-level sentiment analysis can provide a fine-grain sentiment classification for inferring
the sentiment polarity of specific aspects. Graph convolutional network (GCN) becomes …
the sentiment polarity of specific aspects. Graph convolutional network (GCN) becomes …
SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks
Heterogeneous Graph Neural Networks (HGNNs) are extensively applied in modeling web-
based applications that involve heterogeneous graph structures. Explanation models for …
based applications that involve heterogeneous graph structures. Explanation models for …