Let h be a subgraph of g, and the graph obtained by removing all the edges of h is called the relative complement graph of h about g.
The complement of graph G refers to a graph with node set G, and two nodes are connected by an edge if and only if the two nodes are not adjacent to each other on G, in other words, it is a relative complement of G with respect to Kn.
The discrete coefficients can usually be compared with multiple populations, and the comparison of discrete coefficients can explain the representativeness or stability of average index (usually the average value) of different populations. Generally speaking, the smaller the deviation coefficient, the better the representativeness of the average index; The greater the dispersion coefficient, the worse the representativeness of the average index.
Extended data:
The dispersion coefficient reflects the dispersion degree of unit mean, and is often used to compare the dispersion degree of two populations with unequal mean. If the mean values of two populations are equal, the comparison standard deviation coefficient is equivalent to the comparison standard deviation coefficient.
The ratio of the standard deviation of a group of data to its corresponding mean is a relative index to measure the degree of data dispersion, and its function is mainly to compare the degree of data dispersion of different groups.
If graph G(V, e) is disconnected, then its vertices can be divided into two non-empty sets A and B, in which any point P and any point Q in A does not have PQ edges.
If we take its complement G', then there is an edge PQ for any point P and any point Q in A ... For any two points P and Q, if they are in A and B respectively, there is an edge between them; Otherwise, let's assume that they are all in A without loss of generality. Since B is not empty, we can take any R in B. We know that both PR and QR exist, so P and Q are connected.
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