10. Graphlime: Local interpretable model explanations for graph neural networks 11. Graph neural networks in network neuroscience 12. Self-supervised learning of graph neural networks: A unified review 13. Protgnn: Towards self-explaining graph neural networks 14. Graph neural networks for recommender...
Graph Neural Networks in Network Neuroscience[M/OL]. arXiv, 2022[2023-06-29]. http://arxiv.org/abs/2106.03535. [8]吴博, 梁循, 张树森,等. 图神经网络前沿进展与应用[J]. 计算机学报, 2022, 45(1): 35-68. 作者:光影 / 范存源排版:光影| 封面:Tatiana Plakhova 编辑于 2023-07-03 21:06...
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is st...
We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell–cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated ...
The proposed algorithm can also perform well in different graph neural networks. Keywords: federated learning; graph convolutional neural network; non-Euclidean spatial data; attention mechanism MSC: 68T071. Introduction Federated learning [1,2,3] is a particular type of distributed machine learning....
5.1. A Sweet Spot for Top Neural Networks 5.2. Neural Network Performance as a Smooth Function over Graph Measures 5.3. Consistency across Architectures 5.4. Quickly Identifying a Sweet Spot 5.5. Network Science and Neuroscience Connections
5.2. Neural Network Performance as a Smooth Function over Graph Measures 5.3. Consistency across Architectures 5.4. Quickly Identifying a Sweet Spot 5.5. Network Science and Neuroscience Connections ...
show that well-established graph techniques and methodologies offered in other science disciplines (network science, neuroscience, etc.) could contribute to understanding and designing deep neural networks. We believe this could be a fruitful avenue of future research that tackles more complex situations...
Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnect
Hence, they cannot effectively infer the active biological networks of diverse cell types simultaneously with cell clustering and have limited power to elucidate the response of these complex networks to external stimuli in specific cell types. Recently, graph neural networks (GNN) have shown strength...