Semi-Supervised Classification with Graph Convolutional Networks

Semi-supervised classification with graph convolutional networks (GCNs) is a method for predicting labels for nodes in a graph. GCNs are a type of neural network that operates on graph-structured data, where each node in the graph represents an entity (such as a person, a product, or a webpage) and edges represent relationships between entities.

The semi-supervised classification problem arises when we have a graph where only a small subset of nodes have labels, and we want to predict the labels of the remaining nodes. GCNs can be used to solve this problem by learning to propagate information through the graph, using the labeled nodes as anchors.

The key idea behind GCNs is to use a graph convolution operation to aggregate information from a node's neighbors, and then use this aggregated information to update the node's representation. This operation is then repeated over multiple layers, allowing the network to capture increasingly complex relationships between nodes.

To train a GCN for semi-supervised classification, we use a combination of labeled and unlabeled nodes as input, and optimize a loss function that encourages the network to correctly predict the labels of the labeled nodes while also encouraging the network to produce smooth predictions across the graph.

Overall, semi-supervised classification with GCNs is a powerful and flexible method for predicting labels on graph-structured data, and has been successfully applied to a wide range of applications including social network analysis, drug discovery, and recommendation systems.