Graph-based methods are a type of analytical approach that uses graph theory to model and analyze relationships between entities in a dataset. This method represents data as a graph structure, where nodes represent entities and edges represent relationships between them. Graph-based methods are commonly used in various fields such as social network analysis, recommender systems, and bioinformatics. These methods allow researchers to uncover patterns and insights in interconnected data by examining the structure and properties of the graph. Examples of graph-based methods include graph clustering, community detection, and centrality analysis. These methods are particularly useful for understanding complex relationships and networks in large datasets.