# graph attention network

> graph neural network with attention

**Wikidata**: [Q111725450](https://www.wikidata.org/wiki/Q111725450)  
**Source**: https://4ort.xyz/entity/graph-attention-network

## Summary
A graph attention network (GAT) is a type of artificial neural network that uses an attention mechanism. As a subclass of graph neural networks, it is a computational model designed for machine learning tasks that involve data structured as a graph. Its defining feature is its ability to weigh the importance of different nodes within a graph.

## Key Facts
- **Alias:** Commonly known as GAT.
- **Primary Mechanism:** Uses an attention mechanism.
- **Classification:** A subclass of artificial neural networks.
- **Type:** A graph neural network.
- **Defining Source:** Described in the academic paper "Graph Attention Networks".
- **Parent Concept:** Belongs to the category of artificial neural networks, which are computational models used in machine learning.

## FAQs
### Q: What is a GAT?
A: A GAT, or graph attention network, is a type of artificial neural network. It is a specific kind of graph neural network that is distinguished by its use of an attention mechanism to process data.

### Q: What is the main feature of a graph attention network?
A: The main feature of a graph attention network is its use of an attention mechanism. This allows the model to focus on more important parts of the graph data when performing computations.

### Q: What kind of model is a graph attention network?
A: A graph attention network is a computational model used for machine learning. It is classified as an artificial neural network, which is a model based on connected, hierarchical functions.

## Why It Matters
The significance of the graph attention network (GAT) lies in its specific architecture within the broader field of machine learning. As a type of graph neural network, it is designed to work with data structured as graphs, a common format for representing relationships in social networks, molecular biology, and other complex systems.

The key innovation of the GAT is its incorporation of an attention mechanism. This allows the model to selectively focus on the most relevant nodes and edges in a graph when making predictions or classifications, rather than treating all neighbors equally. By combining the graph-based structure of a GNN with the selective focus of attention, the GAT represents a specific and powerful approach for building more effective computational models. Its formal description in the paper "Graph Attention Networks" established it as a distinct and influential architecture in the development of neural networks for graph data.

## Notable For
- **Use of Attention:** Its defining characteristic is the integration of an attention mechanism into a graph neural network architecture, allowing it to weigh the importance of neighboring nodes.
- **Specific Subclass:** It is a distinct subclass of artificial neural networks, specifically designed to operate on graph-structured data.
- **Formal Description:** The model and its architecture are formally described and named in the academic source "Graph Attention Networks".

## Body
### Classification
A graph attention network (GAT) is a type of artificial neural network and a subclass of the broader category of graph neural networks. As an artificial neural network, it is a computational model used in machine learning that is based on connected, hierarchical functions.

### Core Components
The primary component that defines a graph attention network is its use of an attention mechanism. This mechanism is applied to the nodes within the graph structure to inform the model's computations.

### Naming and Aliases
- The model is commonly referred to by the alias GAT.
- Its formal description is provided by the source "Graph Attention Networks".