GCN Based

Graph Convolutional Networks (GCNs) are a powerful class of neural networks designed to process graph-structured data, addressing limitations of traditional methods in handling complex relationships between data points. Current research focuses on improving GCN efficiency and scalability through optimized architectures like PointNet++, hardware acceleration (e.g., FPGA implementations), and novel training strategies such as distributed training and sampling techniques. These advancements are enabling the application of GCNs to increasingly complex problems across diverse fields, including action recognition, traffic flow prediction, and collaborative filtering, with a strong emphasis on improving accuracy, speed, and robustness to noise and adversarial attacks.