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In graph theory and computer science, an adjacency matrix is a square matrix used to represent a finite graph. The elements of the matrix indicate whether pairs of vertices are adjacent or not in the graph. In the special case of a finite simple graph, the adjacency matrix is a (0,1)-matrix with zeros on its diagonal.
In an adjacency matrix, this operation takes time proportional to the number of vertices in the graph, which may be significantly higher than the degree. On the other hand, the adjacency matrix allows testing whether two vertices are adjacent to each other in constant time; the adjacency list is slower to support this operation.
Adjacency lists are generally preferred for the representation of sparse graphs, while an adjacency matrix is preferred if the graph is dense; that is, the number of edges | | is close to the number of vertices squared, | |, or if one must be able to quickly look up if there is an edge connecting two vertices.
For, the adjacency matrix of a directed graph with n vertices can be any (0,1) matrix of size , which can then be reinterpreted as the adjacency matrix of a bipartite graph with n vertices on each side of its bipartition. [27] In this construction, the bipartite graph is the bipartite double cover of the directed graph.
In terms of the adjacency matrix A of the graph, if Q is the adjacency matrix of the complete graph of the same number of vertices (i.e. all entries are unity except the diagonal entries which are zero), then the adjacency matrix of the complement of A is Q-A. The complement is not defined for multigraphs.
The adjacency matrix distributed between multiple processors for parallel Prim's algorithm. In each iteration of the algorithm, every processor updates its part of C by inspecting the row of the newly inserted vertex in its set of columns in the adjacency matrix. The results are then collected and the next vertex to include in the MST is ...
represents the edge weight between nodes i and j; see Adjacency matrix; and are the sum of the weights of the edges attached to nodes i and j, respectively; m is the sum of all of the edge weights in the graph; N is the total number of nodes in the graph;
The fundamental operation of Karger’s algorithm is a form of edge contraction.The result of contracting the edge = {,} is a new node .Every edge {,} or {,} for {,} to the endpoints of the contracted edge is replaced by an edge {,} to the new node.