Graph

Graphs are data structures that represent relationships between entities using nodes (vertices) and edges (connections).

They are used when there is a need to model complex relationships and interactions between entities, such as social networks, molecular structures, and transportation systems. Graphs are commonly applied in scenarios such as network analysis, recommendation systems, and fraud detection. The technique works by representing entities as nodes and their relationships as edges, allowing models to analyze the structure and properties of the graph.

For example, in social network analysis, individuals can be represented as nodes and their follow-relations as edges. In molecular structures, atoms can be represented as nodes and chemical bonds as edges.

Graph creation is a special use case of machine learning where models are used to infer structured representations of data. These graphs are then used to organize, visualize, and navigate knowledge in complex systems. For instance, in recommendation systems, a graph can be created to represent user-item interactions, which can then be used to recommend items to the user.

Graphs are also used as input to Graph Neural Networks (GNNs), which are specialized neural networks designed to process graph-structured data. GNNs can learn to capture the dependencies and relationships between nodes, enabling tasks such as node classification, link prediction, and graph classification.

They are a powerful approach in machine learning, because they provide a way to represent and analyze complex relationships and interactions, enabling models to extract meaningful insights from structured data.