# Graph Neural Networks

## Overview

Graph Neural Networks (GNNs) are a type of neural network that can be used to perform analysis on graph-structured data. GNNs are particularly well-suited for analyzing the Knowledge Graph in Adam.

## Potential Applications

GNNs could be used in Adam to:

*   Predict missing links in the Knowledge Graph.
*   Identify complex patterns and relationships in the data.
*   Improve the accuracy of link prediction and node classification tasks.

## Challenges

*   **Scalability:** GNNs can be computationally expensive to train on large graphs.
*   **Data sparsity:** The Knowledge Graph in Adam may be sparse, which can make it difficult to train a GNN.
*   **Interpretability:** The predictions of GNNs can be difficult to interpret.