Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

1 Department of Computer Engineering, School of Engineering, The University of Jordan, Amman, Jordan. 2 Department of Data Science and Artificial Intelligence, Faculty of Information Technology, ...

Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea ...

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...

Abstract: Attribute graph clustering is a fundamental and challenging task in graph data mining, requiring the adequate utilization of both node attributes and graph structure. Recently, a series of ...