Fake news detection: A survey of graph neural network methods

HT Phan, NT Nguyen, D Hwang - Applied Soft Computing, 2023 - Elsevier
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 …

Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network

X Mo, Z Huang, Y **ng, C Lv - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …

A survey of trustworthy representation learning across domains

R Zhu, D Guo, D Qi, Z Chu, X Yu, S Li - ACM Transactions on …, 2024 - dl.acm.org
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 …

Are graph convolutional networks with random weights feasible?

C Huang, M Li, F Cao, H Fujita, Z Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Multigraph fusion for dynamic graph convolutional network

J Gan, R Hu, Y Mo, Z Kang, L Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Graph convolutional network (GCN) outputs powerful representation by considering the
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 …

GPCNDTA: prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores

L Zhang, CC Wang, Y Zhang, X Chen - Computers in Biology and Medicine, 2023 - Elsevier
Drug-target affinity prediction is a challenging task in drug discovery. The latest
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

J Bordes, L Miranda, M Reinhardt, S Narayan… - Nature …, 2023 - nature.com
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 …

RAGCN: Region aggregation graph convolutional network for bone age assessment from X-ray images

X Li, Y Jiang, Y Liu, J Zhang, S Yin… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

NF-GNN: network flow graph neural networks for malware detection and classification

J Busch, A Kocheturov, V Tresp, T Seidl - Proceedings of the 33rd …, 2021 - dl.acm.org
Malicious software (malware) poses an increasing threat to the security of communication
systems as the number of interconnected mobile devices increases exponentially. While …