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

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

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

Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin and Yong Li. Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution. Figure 1. The architecture of DGCRN.

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 ...

To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of ...

Diving deeper into DLSS 4 reveals just how impressive it is -- and just how problematic it could be in the future.

Turing's 1950 paper didn't just pose the profound question, "Can machines think?". It ignited a quest to build AI technology ...

The RTX 5090 is the most insane graphics card Nvidia has ever released, and that's exactly why it's so impressive.

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 ...

What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...