Hypergraphs are a generalization of traditional graphs in which edges can connect more than two vertices. In a hypergraph, an edge, also known as a hyperedge, can contain any number of vertices. This allows for more complex relationships between vertices to be represented, making hypergraphs a useful tool for modeling various real-world scenarios such as social networks, biological systems, and transportation networks. Hypergraphs have been studied in computer science, mathematics, and other fields for their applications in data mining, machine learning, network analysis, and optimization. Research in the field of hypergraphs focuses on developing algorithms, tools, and methods to analyze and manipulate hypergraph data effectively. This includes exploring properties of hypergraphs, developing data structures for efficient storage and retrieval of hypergraph data, and designing algorithms for tasks such as clustering, pattern mining, and visualization of hypergraphs.