adjacency matrix

Let A be the adjacency matrix of a network under consideration.

It is also the adjacency matrix of the two-graph associated with G.

Since is a bipartite graph, we may consider its adjacency matrix.

A better estimate of adjacency matrix may produce variations in the indices.

This results in a directed weighted adjacency matrix, of a fully connected network.

With an adjacency matrix, an entire row must instead be scanned, which takes O(n) time.

In particular, the adjacency matrix of a strongly connected graph is irreducible.

The adjacency matrix of an empty graph is a zero matrix.

The relations are described by their adjacency matrices.

The p-numbers are the eigenvalues of the adjacency matrix .

This is the adjacency matrix of a two-graph.

For use as a data structure, the main alternative to the adjacency matrix is the adjacency list.

This quality factor is determined by the eigenvectors of the adjacency matrix of the network.

The result is another adjacency matrix, which stores the links for a network described by all the links in the parents.

If the graph is undirected, the adjacency matrix is symmetric.

The adjacency matrix of a complete graph contains all ones except along the diagonal where there are only zeros.

The adjacency matrix A of a strongly regular graph satisfies these properties :

Suppose two directed or undirected graphs and with adjacency matrices and are given.

The main diagonal of every adjacency matrix corresponding to a graph without loops has all zero entries.

We consider an undirected network with 10 nodes and 12 edges and the following adjacency matrix.

A forest of stars is a set of star worlds whose adjacency matrix is a tree.

Given a graph with adjacency matrix the alpha centrality is defined as follows:

The Dependency matrix is the weighted adjacency matrix, representing the fully connected network.

While the adjacency matrix depends on the vertex labeling, its spectrum is a graph invariant.

The other significant difference between adjacency lists and adjacency matrices is in the efficiency of the operations they perform.