Примери коришћења Adjacency на Енглеском и њихови преводи на Српски
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The adjacency matrix of an empty graph is a zero matrix.
If the graph is undirected, the adjacency matrix will be symmetric.
In an adjacency list, the neighbors of each vertex may be listed efficiently, in time proportional to the degree of the vertex.
If our graph is undirected,then the adjacency matrix is symmetric.
Types of associations by adjacency are also present when memorizing verbal information, when learning poems or languages.
It is used to establish and maintain adjacency with other OSPF routers.
The adjacency matrix and Laplacian appear most frequently but also the signless Laplacian as well as normalized versions of these matrices.
For use as a data structure,the main alternative to the adjacency list is the adjacency matrix.
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.
The main insight is that every non-zero term in the Pfaffian of the adjacency matrix of a graph G corresponds to a perfect matching.
However, for a large sparse graph, adjacency lists require less storage space, because they do not waste any space to represent edges that are not present.
The sum of weighted perfect matchings can also be computed by using the Tutte matrix for the adjacency matrix in the last step.
The other significant difference between adjacency lists and adjacency matrices is in the efficiency of the operations they perform.
In spectral graph theory we expect the result relate to all subjects quoted in the research description andespecially results in relation with extremal problems with eigenvalues for the adjacency, Laplacian and signless Laplacian natrix.
Return the absolute value of the Pfaffian of the(1,- 1, 0)-adjacency matrix of G, which is the square root of the determinant.
This version of the adjacency list uses more memory than the version in which adjacent vertices are listed directly, but the existence of explicit edge objects allows it extra flexibility in storing additional information about edges.
The relationship between a graph andthe eigenvalues and eigenvectors of its adjacency matrix is studied in spectral graph theory.
Because a DAG cannot have self-loops, its adjacency matrix must have a zero diagonal, so adding I preserves the property that all matrix coefficients are 0 or 1.
In other words, the total time to report all of the neighbors of a vertex v is proportional to the degree of v. It is also possible, but not as efficient,to use adjacency lists to test whether an edge exists or does not exist between two specified vertices.
Frequently used graph matrices are the adjacency matrix A, the Laplacian L and the signless Laplacian Q=D+A, where D is a diagonal matrix of vertex degrees.
In an adjacency list in which the neighbors of each vertex are unsorted, testing for the existence of an edge may be performed in time proportional to the minimum degree of the two given vertices, by using a sequential search through the neighbors of this vertex.
Whether there is an edge between two given vertices can be determined at once with an adjacency matrix, while requiring time proportional to the minimum degree of the two vertices with the adjacency list.
A similar reinterpretation of adjacency matrices may be used to show a one-to-one correspondence between directed graphs(on a given number of labeled vertices, allowing self-loops) and balanced bipartite graphs, with the same number of vertices on both sides of the bipartition.
The proof is bijective:a matrix A is an adjacency matrix of a DAG if and only if A+ I is a(0,1) matrix with all eigenvalues positive, where I denotes the identity matrix.
For, the adjacency matrix of a directed graph with vertices can be any-matrix of size, which can then be reinterpreted as the adjacency matrix of a bipartite graph with vertices on each side of its bipartition.[23] In this construction, the bipartite graph is the bipartite double cover of the directed graph.
A simple implementation using an adjacency matrix graph representation and searching an array of weights to find the minimum weight edge to add requires O(V2) running time.
The main alternative to the adjacency list is the adjacency matrix, a matrix whose rows and columns are indexed by vertices and whose cells contain a Boolean value that indicates whether an edge is present between the vertices corresponding to the row and column of the cell.
When the graph is stored in the form of adjacency list or matrix, priority queue can be used to extract minimum efficiently when implementing Dijkstra's algorithm, although one also needs the ability to alter the priority of a particular vertex in the priority queue efficiently.
This mixture is designed to isolate the adjacencies.
Two adjacencies that are not conflicting are called compatible.