Graph Partitioning is a technique used in computer science and mathematics to divide a graph into smaller, more manageable parts, or "partitions." Each partition contains a subset of the graph's vertices, and the goal is to minimize the number of edges that connect vertices in different partitions. This is useful for optimizing resource allocation, parallel processing, and improving the efficiency of algorithms. Applications of graph partitioning can be found in various fields, including network design, data mining, and machine learning. By breaking down complex problems into simpler components, graph partitioning helps enhance performance and scalability in large-scale systems.