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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 graph theory, a branch of mathematics, the k th power G k of an undirected graph G is another graph that has the same set of vertices, but in which two vertices are adjacent when their distance in G is at most k. Powers of graphs are referred to using terminology similar to that of exponentiation of numbers: G 2 is called the square of G, G ...
For instance, a graph consisting of a square of branches would still be the same topological graph if two branches were interchanged unless the branches were uniquely labelled. In directed graphs, the two nodes that a branch connects to are designated the source and target nodes. Typically, these will be indicated by an arrow drawn on the ...
Adjacency matrix — a square matrix representing a graph, with a ij non-zero if vertex i and vertex j are adjacent. Biadjacency matrix — a special class of adjacency matrix that describes adjacency in bipartite graphs. Degree matrix — a diagonal matrix defining the degree of each vertex in a graph. Edmonds matrix — a square matrix of a ...
A Cartesian product of two graphs. In graph theory, the Cartesian product G H of graphs G and H is a graph such that: the vertex set of G H is the Cartesian product V(G) × V(H); and; two vertices (u,v) and (u' ,v' ) are adjacent in G H if and only if either u = u' and v is adjacent to v' in H, or; v = v' and u is adjacent to u' in G.
In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]
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The Seidel matrix of G is also the adjacency matrix of a signed complete graph K G in which the edges of G are negative and the edges not in G are positive. It is also the adjacency matrix of the two-graph associated with G and K G. The eigenvalue properties of the Seidel matrix are valuable in the study of strongly regular graphs.