Graph Neural Network

Graph Neural Networks (GNNs) are a class of neural networks designed to perform inference on data described by graphs.

GNNs are used in various applications such as social network analysis, recommendation systems, and molecular biology. They operate on graph structures, where nodes represent entities and edges represent relationships between these entities. GNNs learn to aggregate and transform node features based on the graph’s connectivity, enabling them to capture complex dependencies and patterns.

For example, in a social network, a GNN can predict user behavior by considering the connections and interactions between users. The model aggregates information from a user’s neighbors to make predictions about the user’s preferences or actions.

Gated Graph Networks (GGNs) are a variant of GNNs that incorporate gating mechanisms to control the flow of information through the network. These gating mechanisms, help GGNs handle long-range dependencies by controlling how much of the input is added to the current state, what portion of the previvious state should be discarded, and how much of the current state is propagated to the next state.

In summary, GNNs are powerful tools for analyzing and making predictions on graph-structured data. They are known for their ability to capture intricate relationships and dependencies within graphs, making them essential in fields like social network analysis and bioinformatics. Gated Graph Networks enhance the capabilities of GNNs by incorporating gating mechanisms, allowing for better handling of long-range dependencies and more accurate predictions.