Using a novel deep learning approach, we evaluated the predictive power of the functional connectome during various states (resting state, movie-watching, and n-back) on episodic memory and working ...

We propose a connectivity-based graph convolution network (cGCN ... "Application of convolutional recurrent neural network for individual recognition based on resting state fmri data." Frontiers in ...

Abstract: Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

To address these issues, we propose a new GNN algorithm, LEGNN (Local and Global Enhanced Graph Neural Network), which introduces several key improvements over traditional GNN models such as GraphSAGE ...

For an introduction to the paper check out my blog post. Also checkout my blog post about using Text GCN to classify tweets for asian prejudice during COVID-19. Text classification is an important and ...

Graph convolutional network (GCN) (Kipf et al., 2017) extends the convolutional neural network to solve non-Euclidean space problems. It uses structural information on the constructed network by ...

GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...