Graph neural networks and their current applications in bioinformatics
XM Zhang, L Liang, L Liu, MJ Tang - Frontiers in genetics, 2021 - frontiersin.org
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …
perform particularly well in various tasks that process graph structure data. With the rapid …
Explainable artificial intelligence applications in cyber security: State-of-the-art in research
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Atomistic line graph neural network for improved materials property predictions
Graph neural networks (GNN) have been shown to provide substantial performance
improvements for atomistic material representation and modeling compared with descriptor …
improvements for atomistic material representation and modeling compared with descriptor …
A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
A generalization of vit/mlp-mixer to graphs
Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …
representation learning. Standard GNNs define a local message-passing mechanism which …
Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Graph neural networks in network neuroscience
Noninvasive medical neuroimaging has yielded many discoveries about the brain
connectivity. Several substantial techniques map** morphological, structural and …
connectivity. Several substantial techniques map** morphological, structural and …
MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis
Purpose Recently, functional brain networks (FBN) have been used for the classification of
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
Braingb: a benchmark for brain network analysis with graph neural networks
Map** the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …