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Fake news detection: A survey of graph neural network methods
The emergence of various social networks has generated vast volumes of data. Efficient
methods for capturing, distinguishing, and filtering real and fake news are becoming …
methods for capturing, distinguishing, and filtering real and fake news are becoming …
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …
for safe and efficient operation of connected automated vehicles under complex driving …
A survey of trustworthy representation learning across domains
As AI systems have obtained significant performance to be deployed widely in our daily lives
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
Are graph convolutional networks with random weights feasible?
Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …
are receiving extensive attention for their powerful capability in learning node …
Multigraph fusion for dynamic graph convolutional network
Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …
structure information of the data to conduct representation learning, but its robustness is …
Exploring self-attention graph pooling with EEG-based topological structure and soft label for depression detection
T Chen, Y Guo, S Hao, R Hong - IEEE transactions on affective …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) has been widely used in neurological disease detection, ie,
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
GPCNDTA: prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores
Drug-target affinity prediction is a challenging task in drug discovery. The latest
computational models have limitations in mining edge information in molecule graphs …
computational models have limitations in mining edge information in molecule graphs …
Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress
Severe stress exposure increases the risk of stress-related disorders such as major
depressive disorder (MDD). An essential characteristic of MDD is the impairment of social …
depressive disorder (MDD). An essential characteristic of MDD is the impairment of social …
RAGCN: Region aggregation graph convolutional network for bone age assessment from X-ray images
Rapid and accurate measurement of bone age from hand X-ray images is a significant task
for children's maturity assessment and metabolic disorders diagnosis. With the development …
for children's maturity assessment and metabolic disorders diagnosis. With the development …
NF-GNN: network flow graph neural networks for malware detection and classification
Malicious software (malware) poses an increasing threat to the security of communication
systems as the number of interconnected mobile devices increases exponentially. While …
systems as the number of interconnected mobile devices increases exponentially. While …