Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different ...

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

Abstract: Deep graph convolutional networks (GCNs) have shown promising performance in ... Second, the computational complexity of dynamic graph convolution operations grows quadratically with the ...

We propose a supervised data-driven method to predict S-wave velocity using a graph convolutional network with a bidirectional gated recurrent unit (GCN-BiGRU). This method adopts the total ...

These challenges can lead to inaccurate brain network representations and potential misdiagnoses.To address these challenges, we propose BrainDGT, a dynamic Graph Transformer model designed to enhance ...

Nature Research Intelligence Topics enable transformational understanding and discovery in research by categorising any document into meaningful, accessible topics. Read this blog to understand ...

Finally, we introduced a dynamic activation mechanism to automatically adjust ... To tackle this, we proposed PoseGCN, a Graph Convolutional Network-based model designed to integrate spatial, temporal ...

GraphPro is a versatile and pluggable OO python library designed for leveraging deep graph learning representations to gain insights into structural proteins and ...