A survey on graph counterfactual explanations: definitions, methods, evaluation, and research challenges
Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
Deep learning in food authenticity: Recent advances and future trends
Background The development of fast, efficient, accurate, and reliable techniques and
methods for food authenticity identification is crucial for food quality assurance. Traditional …
methods for food authenticity identification is crucial for food quality assurance. Traditional …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …
routinely solve complex problems on unstructured networks, such as node classification …
Fast graph condensation with structure-based neural tangent kernel
The rapid development of Internet technology has given rise to a vast amount of graph-
structured data. Graph Neural Networks (GNNs), as an effective method for various graph …
structured data. Graph Neural Networks (GNNs), as an effective method for various graph …
[HTML][HTML] Deep graphical regression for jointly moderate and extreme Australian wildfires
Recent wildfires in Australia have led to considerable economic loss and property
destruction, and there is increasing concern that climate change may exacerbate their …
destruction, and there is increasing concern that climate change may exacerbate their …
A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism
Z Zhang, Q Meng, LC Jin, H Wang, H Hou - Expert Systems with …, 2024 - Elsevier
Electroencephalography (EEG) is capable of capturing the evocative neural information
within the brain. As a result, it has been increasingly used for identifying neurological …
within the brain. As a result, it has been increasingly used for identifying neurological …
A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection
For strip surface defect detection, the key is to achieve reliable detection results with high
detection speed. This paper mainly focuses on the ability to distinguish defects with similar …
detection speed. This paper mainly focuses on the ability to distinguish defects with similar …
Variable correlation analysis-based convolutional neural network for far topological feature extraction and industrial predictive modeling
In process industries, accurate prediction of critical quality variables is particularly important
for process control and optimization. Usually, soft sensors have been developed to estimate …
for process control and optimization. Usually, soft sensors have been developed to estimate …
[HTML][HTML] Dynamic network link prediction with node representation learning from graph convolutional networks
P Mei, YH Zhao - Scientific Reports, 2024 - nature.com
Dynamic network link prediction is extensively applicable in various scenarios, and it has
progressively emerged as a focal point in data mining research. The comprehensive and …
progressively emerged as a focal point in data mining research. The comprehensive and …
Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network
The present study introduces a novel optimization framework that combines a Graph
Convolutional Neural Network surrogate model with Genetic Algorithms (GCN-GA). This …
Convolutional Neural Network surrogate model with Genetic Algorithms (GCN-GA). This …