Abstract: Graph convolutional networks (GCNs ... approach to skeleton-based action recognition named Multi-stage Adaptive Graph Convolution Network (MSA-GCN). It consists of two modules: Multi-stage ...

Defeating Wi-Fi dead zones is more important than ever. A mesh network system can help, and these are the best we've tested. I’ve been working with computers for ages, starting with a multi-year ...

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 Network Want to learn ...

Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes’ global ...

Everyone should be able to use basic Windows 10 network commands entered from the command prompt to troubleshoot network connection problems. Living and working in an always-connected world means ...

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). Official PyTorch implementation of Superpoint Transformer ...

@article{gao2020accurate, title={Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures}, author={Gao, Peng and ...

Graph Convolutional Networks (GCNs) have become integral in analyzing complex graph-structured data. These networks capture the relationships between nodes and their attributes, making them ...