graph-convolutional-network

Graph Convolutional Networks (GCNs) extend convolutional neural networks to work on graph-structured data, enabling the learning of node embeddings by aggregating information from neighboring nodes. A common misconception is that GCNs are only suitable for social networks; they can be applied in various fields such as chemistry and recommendation systems by modeling relationships as graphs.