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This article mainly introduces "how to realize the temporary matrix in the database". In daily operation, I believe many people have doubts about how to realize the temporary matrix in the database. I have consulted all kinds of materials and sorted out simple and easy operation methods. I hope to help you answer the doubts about "how to realize the temporary matrix in the database"! Next, please follow the small series to learn together!

A graph consists of vertices and edges or arcs. Vertices are represented by one-dimensional arrays regardless of size. Edges or arcs are stored in two-dimensional arrays, which are adjacency matrices.

If G(V,E) has N vertices, then the adjacent matrix is N*N square matrix.

Maintains a two-dimensional array where arr[i][j] denotes edges from i to j, 1 if edges exist between vertices, 0 otherwise; undirected graphs are symmetric matrices

Maintain a one-dimensional array that stores vertex information, such as vertex names;

1. Features of adjacency matrix (undirected graph):

The adjacency matrix of a graph is stored in two arrays:

1.）A one-dimensional array stores information about vertices in a graph.

2.）A two-dimensional array (called an adjacency matrix) stores information about edges or arcs in a graph.

In the image above, we set up two arrays:

Vertex[4] = {V0, V1, V2, V3}

Edge array: arc[4][4] is a symmetric matrix (0 means there are no edges between vertices, 1 means there are edges between vertices)

2. Features of adjacency matrix (directed graph):

The edges of an undirected graph form a symmetric matrix, which seems to waste half of the space. If it is a directed graph to store, will it make good use of resources?

Vertex array vertex[4] = {V0, V1, V2, V3}

The arc array arc[4][4] is also a matrix, but because it is a directed graph, the matrix is not symmetric.

For example: V1 to V0 arc, so arc[1][0] = 1, and V0 to V1 no arc, so arc[0][1] = 0

3. Characteristics of adjacency matrix (net):

A graph with weights on each edge is called a net. Usually the weight represents the distance between two points.

Here ∞ denotes the value allowed by a computer that is greater than all edge weights.

Define the structure of adjoint matrix graph typedef char Vtype;//vertex typedef int Etype;//Weight type on edge or arc #define MAXV 100 //Maximum number of vertices #define UNexist 65535 //Weight typedef struct { Vtype VArr[MAXV];//Vertex array Etype EArr[MAXV][MAXV];//adjacency matrix int numV;//Number of vertices int numE;//current number of edges}MGraph; construct a graph void CreateMGraph(MGraph* G){ int i,j,k,w; cout G->numV; cout G->numE; for(i=0;inumV;i++) { cin>> G->VArr[i];//Enter vertex information; } for(i=0;inumV;i++)//initialize the adjacency matrix to null { for(j=0;jnumV;j++) { G->EArr[i][j] = UNexist; } } for(k=0;knumE;k++) { couti; coutj; coutw; G->VArr[i][j]=W; G->VArr[i][j]=G->VArr[j][i]//Symmetry of undirected graphs //If it's not symmetrical, just a line of comments above }}

V vertices and E undirected edges create a graph with O( V^2) time complexity. The adjacency matrix is a good graph storage structure, but if there are few edges between vertices V, there is a lot of wasted space.

At this point, the study of "how to realize the adjacency matrix in database" is over, and I hope to solve everyone's doubts. Theory and practice can better match to help everyone learn, go and try it! If you want to continue learning more relevant knowledge, please continue to pay attention to the website, Xiaobian will continue to strive to bring more practical articles for everyone!

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