### What is a GNN, or graph neural network?

A unique class of neural networks called graph neural networks can operate using a graph data format. Convolutional Neural Networks (CNNs) and graph embedding have a significant impact on them. GNNs are applied to tasks involving graphs, edges, and nodes prediction.

- Images are classified using CNNs. In a similar manner, to predict a class, GNNs are applied to a graph structure, or grid of pixels.
- Text classification using Recurrence Neural Networks. GNNs are also used on graph architectures in which each word in a sentence is a node.
- Convolutional Neural Networks’ inability to produce optimal results due to the graph’s arbitrary size and complex structure led to the introduction of GNNs.

### Graph Neural Network Task Types

Here are some examples of the different kinds of GNN tasks:

- Graph classification is the process by which we divide graphs into different groups. Text classification and social network analysis are two of its uses.
- Node Classification: this task predicts missing node labels in a graph by utilizing surrounding node labels.
- Link prediction uses an imperfect adjacency matrix to predict the link between two nodes in a graph. Social networks are the usual usage for it.
- Community Detection: uses edge structure to group nodes into different groups. It gains knowledge from edge weights, distance, and graph objects in a comparable way.
- Graph embedding is a mapping technique that converts graphs into vectors while maintaining all pertinent node, edge, and structure data.
- Graph Generation: creates graphs by using sample graph distribution as a guide a novel yet comparable graph structure.

### How do Graph Neural Networks work?

Now that we know what graphs are, we can focus on how GNNs use them to solve problems. Let’s start with a very simple example and stick to the topic of recommender systems. Suppose we want to predict which item we should recommend to a user, so as to maximize the chances of that user buying it. Let’s assume that we already have encoded the features of the users and items into vector embeddings for each node, which are represented with the colorful bars in the diagram below. Vector embeddings of features are simply their numerical representations, which can be more easily consume.

## FAQ’s

**What is a Graph Neural Network (GNN)?**

A Graph Neural Network is a specialized type of neural network designed to operate on graph data structures, effectively handling tasks involving nodes, edges, and their relationships within a graph.

**What types of tasks can GNNs perform?**

GNNs can handle various tasks, including graph classification, node classification, link prediction, community detection, graph embedding, and graph generation, making them versatile for applications like social network analysis and recommendation systems.

**How do GNNs differ from Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)?**

Unlike CNNs, which are suited for grid-like structures like images, and RNNs, which handle sequential data like text, GNNs are designed for arbitrary-sized and complex graph structures, making them ideal for tasks where data is naturally represented as graphs.

**How do GNNs enhance the performance of recommender systems?**

In recommender systems, GNNs predict items to recommend by leveraging vector embeddings of user and item features within a graph structure, optimizing recommendations based on the relationships and interactions between users and items.