A graph ADT consists of a set of nodes (also called vertices) and a set of edges that establish relationships (connections) between the nodes. The graph ADT follows directly from the graph concept from mathematics. Informally, graph G=(V,E) consists of vertices, the elements of V, which are connected by edges, the elements of E. Formally, a graph, G, is defined as an ordered pair, G=(V,E), where V is a set (usually finite) and E is a set consisting of two element subsets of V.

Graph data structures are non-hierarchical and therefore suitable for data sets where the individual elements are interconnected in complex ways. For example, a computer network can be modeled with a graph. Hierarchical data sets can be represented by a binary or nonbinary tree. It is worth mentioning, however, that trees can be seen as a special form of graph.

## Graph Representations

Two main data structures for the representation of graphs are used in practice. The first is called an adjacency list, and is implemented by representing each node as a data structure that contains a list of all adjacent nodes. The second is an adjacency matrix, in which the rows and columns of a two-dimensional array represent source and destination vertices and entries in the array indicate whether an edge exists between the vertices. Adjacency lists are preferred for sparse graphs; otherwise, an adjacency matrix is a good choice. Finally, for very large graphs with some regularity in the placement of edges, a symbolic graph is a possible choice of representation.

## Graph Behaviors

Graph algorithms are a significant field of interest within computer science. Typical operations associated with graphs are: finding a path between two nodes, like depth-first search and breadth-first search and finding the shortest path from one node to another, like Dijkstra's algorithm. A solution to finding the shortest path from each node to every other node also exists in the form of the Floyd-Warshall algorithm.

A directed graph can be seen as a flow network, where each edge has a capacity and each edge receives a flow. The Ford-Fulkerson algorithm is used to find out the maximum flow from a source to a sink in a graph.