In a simple graph, each pair of vertices can be connected by at most one edge. Therefore, the maximum number of edges is n(n-1)/2, which is equal to n(n-1) for n > 1.

A connected graph with n vertices must have at least n-1 edges to ensure that there is a path between every pair of vertices. This is known as the connectivity theorem.

The degree of a vertex is the number of edges that are connected to it. It is also known as the valency of the vertex.

The chromatic number of a graph is the minimum number of colors needed to color the vertices of the graph so that no two adjacent vertices have the same color. This is a fundamental concept in graph theory and has applications in scheduling, resource allocation, and other areas.

The maximum flow in a network is the maximum amount of flow that can be sent from a source vertex to a sink vertex in the network without violating any capacity constraints on the edges. This is a fundamental concept in network flow theory and has applications in transportation, logistics, and other areas.

The minimum cut in a network is the minimum amount of flow that must be removed from the network to disconnect the source vertex from the sink vertex. This is a fundamental concept in network flow theory and has applications in network security, reliability, and other areas.

The shortest path between two vertices in a graph is the path with the smallest total weight between the two vertices. This is a fundamental concept in graph theory and has applications in routing, navigation, and other areas.

A spanning tree of a graph is a subgraph of the graph that includes all of the vertices and some of the edges, such that there is exactly one path between any two vertices in the spanning tree. This is a fundamental concept in graph theory and has applications in network design, clustering, and other areas.

An Eulerian circuit in a graph is a closed walk in the graph that visits every edge exactly once. This is a fundamental concept in graph theory and has applications in scheduling, routing, and other areas.

A Hamiltonian circuit in a graph is a closed walk in the graph that visits every vertex exactly once. This is a fundamental concept in graph theory and has applications in scheduling, routing, and other areas.

The main difference between a directed graph and an undirected graph is that in a directed graph, the edges have a direction, while in an undirected graph, the edges do not have a direction. This means that in a directed graph, you can only traverse an edge in the direction that it is pointing, while in an undirected graph, you can traverse an edge in either direction.

The adjacency matrix of a graph is a matrix that represents the adjacency of the vertices in the graph. It is a square matrix with n rows and n columns, where n is the number of vertices in the graph. The entry in the ith row and jth column of the adjacency matrix is 1 if there is an edge between vertex i and vertex j, and 0 otherwise.

The incidence matrix of a graph is a matrix that represents the incidence of the edges in the graph. It is a matrix with n rows and m columns, where n is the number of vertices in the graph and m is the number of edges in the graph. The entry in the ith row and jth column of the incidence matrix is 1 if edge j is incident to vertex i, and 0 otherwise.

The Laplacian matrix of a graph is a matrix that represents the degrees of the vertices in the graph. It is a square matrix with n rows and n columns, where n is the number of vertices in the graph. The entry in the ith row and jth column of the Laplacian matrix is the degree of vertex i if i = j, and -1 if i is adjacent to j, and 0 otherwise.