Description: Graph clustering is a method of grouping vertices in a graph such that the vertices in the same group are more similar to each other than to those in other groups. This approach is based on the structure and relationships of the graph, where nodes represent entities and edges represent the connections or relationships between them. Through specific algorithms, graph clustering seeks to identify communities or densely connected groups within a graph, allowing for a better understanding of the underlying structure of the data. This method is particularly useful in contexts where relationships are as important as the entities themselves, such as in social networks, computational biology, and network analysis. The main characteristics of graph clustering include the ability to handle both structured and unstructured data, the identification of hidden patterns, and the enhancement of complex data visualization. Additionally, it allows for dimensionality reduction and simplification of information, facilitating subsequent analysis. In summary, graph clustering is a powerful tool in unsupervised learning that helps unravel the complexity of relationships in data, providing valuable insights for decision-making and strategy development.
