A graph is a type of data structure composed of vertices (or nodes) that are connected by edges. Graphs are used to model data in many different domains, including computer networks, social networks, biological networks, and communication networks. Graphs are useful for representing the relationships between objects, such as the paths between different locations or the connections between people in a network.
Graphs can be represented in two ways: through a collection of nodes and edges called an adjacency list, or through a matrix representation called an adjacency matrix. An adjacency list is an array of linked lists which represent the edges connecting each node of the graph. Each list contains a reference to a node and the weight associated with the edge connecting it to another node. On the other hand, an adjacency matrix is a two-dimensional matrix whose entries represent the weights associated with the edges between each node of the graph.
Examples of graph data structures include graphs for solving navigation problems, in which the nodes represent cities and the edges represent roads connecting those cities. Graphs are also used to model electrical circuits, with the nodes representing the components of the circuit and the edges representing the wires connecting those components. Social networks are also commonly modeled as graphs, with the nodes representing the individuals in the network and the edges representing the relationships between them.
In addition to these examples, graphs are used to model many other types of data such as financial transactions, project schedules, and biological pathways. Graphs are also used extensively in artificial intelligence and machine learning to represent and manipulate knowledge.
