A comprehensive survey on graph summarization with graph neural networks
As large-scale graphs become more widespread, more and more computational challenges
with extracting, processing, and interpreting large graph data are being exposed. It is …
with extracting, processing, and interpreting large graph data are being exposed. It is …
Hierarchical FFT-LSTM-GCN based model for nuclear power plant fault diagnosis considering spatio-temporal features fusion
Y Wang, J Liu, G Qian - Progress in Nuclear Energy, 2024 - Elsevier
As safety-critical infrastructure, nuclear power plants (NPPs) require enhanced safety
measures and risk minimization. To achieve this goal and to aid operator decision-making …
measures and risk minimization. To achieve this goal and to aid operator decision-making …
Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints
Drug-induced liver injury (DILI) is a significant issue in drug development and clinical
treatment due to its potential to cause liver dysfunction or damage, which, in severe cases …
treatment due to its potential to cause liver dysfunction or damage, which, in severe cases …
Leveraging brain modularity prior for interpretable representation learning of fMRI
Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous
neural activities in the brain and is widely used for brain disorder analysis. Previous studies …
neural activities in the brain and is widely used for brain disorder analysis. Previous studies …
Graph autoencoders for embedding learning in brain networks and major depressive disorder identification
Brain functional connectivity (FC) networks inferred from functional magnetic resonance
imaging (fMRI) have shown altered or aberrant brain functional connectome in various …
imaging (fMRI) have shown altered or aberrant brain functional connectome in various …
Preserving specificity in federated graph learning for fMRI-based neurological disorder identification
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive
approach to examining abnormal brain connectivity associated with brain disorders. Graph …
approach to examining abnormal brain connectivity associated with brain disorders. Graph …
Knowledge distillation guided interpretable brain subgraph neural networks for brain disorder exploration
The human brain is a highly complex neurological system that has been the subject of
continuous exploration by scientists. With the help of modern neuroimaging techniques …
continuous exploration by scientists. With the help of modern neuroimaging techniques …
Classification of developmental and brain disorders via graph convolutional aggregation
While graph convolution-based methods have become the de-facto standard for graph
representation learning, their applications to disease prediction tasks remain quite limited …
representation learning, their applications to disease prediction tasks remain quite limited …
Age-and Severity-Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures
Autism spectrum disorder (ASD) is characterized by divergent etiological factors,
comorbidities, severity levels, genetic influences, and functional connectivity (FC) patterns in …
comorbidities, severity levels, genetic influences, and functional connectivity (FC) patterns in …
A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction
Graph neural networks (GNNs) have recently grown in popularity for disease prediction.
Existing GNN-based methods primarily build the graph topological structure around a single …
Existing GNN-based methods primarily build the graph topological structure around a single …