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第232行：
第232行： − + − 邻接表的主要替代方法是邻接矩阵矩阵，这是一个矩阵，其行和列按顶点索引，其单元格包含一个布尔值，表明是否有一条边存在于对应于单元格行和列的顶点之间。对于稀疏图(其中大多数顶点对没有连接的边) ，邻接表的空间利用率明显高于邻接矩阵表(存储为二维数组) : 邻接表的空间利用率与图中边和顶点的数量成正比，而以这种方式存储的邻接矩阵表的空间利用率与顶点数的平方成正比。然而，通过使用哈希表索引的顶点对而不是数组，可以更有效地存储邻接矩阵，匹配邻接表的线性空间使用。+ − 第245行：
第244行： − 邻接表和邻接矩阵之间的另一个显著区别是它们执行操作的效率。在邻接表中，每个顶点的邻居可以有效地列出，在时间上与顶点的程度成正比。在邻接矩阵中，这个操作需要的时间与图中顶点的数量成正比，而顶点的数量可能明显高于度。另一方面，邻接邻接矩阵允许测试两个顶点是否在固定的时间内彼此相邻; 邻接表支持这一操作的速度较慢。+ − −
→Trade-offs
==Trade-offs==
==Trade-offs==
权衡
The main alternative to the adjacency list is the [[adjacency matrix]], a [[matrix (mathematics)|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. For a [[sparse graph]] (one in which most pairs of vertices are not connected by edges) an adjacency list is significantly more space-efficient than an adjacency matrix (stored as a two-dimensional array): the space usage of the adjacency list is proportional to the number of edges and vertices in the graph, while for an adjacency matrix stored in this way the space is proportional to the square of the number of vertices. However, it is possible to store adjacency matrices more space-efficiently, matching the linear space usage of an adjacency list, by using a hash table indexed by pairs of vertices rather than an array.
The main alternative to the adjacency list is the [[adjacency matrix]], a [[matrix (mathematics)|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. For a [[sparse graph]] (one in which most pairs of vertices are not connected by edges) an adjacency list is significantly more space-efficient than an adjacency matrix (stored as a two-dimensional array): the space usage of the adjacency list is proportional to the number of edges and vertices in the graph, while for an adjacency matrix stored in this way the space is proportional to the square of the number of vertices. However, it is possible to store adjacency matrices more space-efficiently, matching the linear space usage of an adjacency list, by using a hash table indexed by pairs of vertices rather than an array.
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. For a sparse graph (one in which most pairs of vertices are not connected by edges) an adjacency list is significantly more space-efficient than an adjacency matrix (stored as a two-dimensional array): the space usage of the adjacency list is proportional to the number of edges and vertices in the graph, while for an adjacency matrix stored in this way the space is proportional to the square of the number of vertices. However, it is possible to store adjacency matrices more space-efficiently, matching the linear space usage of an adjacency list, by using a hash table indexed by pairs of vertices rather than an array.
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. For a sparse graph (one in which most pairs of vertices are not connected by edges) an adjacency list is significantly more space-efficient than an adjacency matrix (stored as a two-dimensional array): the space usage of the adjacency list is proportional to the number of edges and vertices in the graph, while for an adjacency matrix stored in this way the space is proportional to the square of the number of vertices. However, it is possible to store adjacency matrices more space-efficiently, matching the linear space usage of an adjacency list, by using a hash table indexed by pairs of vertices rather than an array.
邻接表的主要替代方法是'''<font color="#ff8000">邻接矩阵 Adjacency Matrix </font>'''，该矩阵的行和列按顶点索引，其单元格包含一个布尔值，该值指示与单元格的行和列对应的顶点之间是否存在边。对于'''<font color="#ff8000">稀疏图 Sparse Graph </font>'''（大多数顶点对不是由边连接的图），邻接表比邻接矩阵（存储为二维数组）更节省空间：邻接表的空间使用与图中的边和顶点的数量成正比，而对于以这种方式存储的邻接矩阵，其空间与顶点数的平方成正比。然而，通过使用由顶点对索引的哈希表而不是数组，可以更有效地存储邻接矩阵的空间，从而匹配邻接表的线性空间使用。
The other significant difference between adjacency lists and adjacency matrices is in the efficiency of the operations they perform. In an adjacency list, the neighbors of each vertex may be listed efficiently, in time proportional to the degree of the vertex. 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.
The other significant difference between adjacency lists and adjacency matrices is in the efficiency of the operations they perform. In an adjacency list, the neighbors of each vertex may be listed efficiently, in time proportional to the degree of the vertex. 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.
邻接表和邻接矩阵之间的另一个显著区别是它们执行操作的效率。在邻接表中，每个顶点的邻居可以有效地列出，在时间上与顶点的程度成正比。在邻接矩阵中，这个操作需要的时间与图中顶点的数量成正比，而顶点的数量可能明显高于度。另一方面，邻接矩阵允许测试两个顶点是否在固定的时间内彼此相邻; 邻接表支持这一操作的速度较慢。
==Data structures==
==Data structures==