Graph Neural Networks

What are Graph Neural Networks?

Graph Neural Networks (GNNs) are a class of deep learning models designed to work with graph-structured data. GNNs are particularly useful for tasks involving relational or spatial data, such as social network analysis, molecular modeling, and computer vision. GNNs have shown promising results in various applications, including node classification, link prediction, and graph generation.

How do Graph Neural Networks work?

GNNs work by performing message-passing and aggregation operations on the nodes and edges of the graph. These operations involve updating the node features based on the features of their neighbors and the edge attributes. GNNs typically consist of multiple layers, each responsible for aggregating information from a larger neighborhood. After the final layer, GNNs can produce node, edge, or graph-level outputs, depending on the task.

Resources for learning Graph Neural Networks: