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<h1 id="91">9.1 &nbsp; 图<a class="headerlink" href="#91" title="Permanent link">&para;</a></h1>
<p>「图 graph」是一种非线性数据结构，由「顶点 vertex」和「边 edge」组成。我们可以将图 <span class="arithmatex">\(G\)</span> 抽象地表示为一组顶点 <span class="arithmatex">\(V\)</span> 和一组边 <span class="arithmatex">\(E\)</span> 的集合。以下示例展示了一个包含 5 个顶点和 7 条边的图。</p>
<div class="arithmatex">\[
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<h1 id="92">9.2 &nbsp; 图基础操作<a class="headerlink" href="#92" title="Permanent link">&para;</a></h1>
<p>图的基础操作可分为对“边”的操作和对“顶点”的操作。在“邻接矩阵”和“邻接表”两种表示方法下，实现方式有所不同。</p>
<h2 id="921">9.2.1 &nbsp; 基于邻接矩阵的实现<a class="headerlink" href="#921" title="Permanent link">&para;</a></h2>
@@ -5582,11 +5614,73 @@
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<p>观察表 9-2 ，似乎邻接表（哈希表）的时间与空间效率最优。但实际上，在邻接矩阵中操作边的效率更高，只需要一次数组访问或赋值操作即可。综合来看，邻接矩阵体现了“以空间换时间”的原则，而邻接表体现了“以时间换空间”的原则。</p>
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<h1 id="93">9.3 &nbsp; 图的遍历<a class="headerlink" href="#93" title="Permanent link">&para;</a></h1>
<p>树代表的是“一对多”的关系，而图则具有更高的自由度，可以表示任意的“多对多”关系。因此，我们可以把树看作是图的一种特例。显然，<strong>树的遍历操作也是图的遍历操作的一种特例</strong>。</p>
<p>图和树都都需要应用搜索算法来实现遍历操作。图的遍历方式可分为两种：「广度优先遍历 breadth-first traversal」和「深度优先遍历 depth-first traversal」。它们也常被称为「广度优先搜索 breadth-first search」和「深度优先搜索 depth-first search」，简称 BFS 和 DFS 。</p>
@@ -4313,11 +4345,73 @@
<p><strong>时间复杂度：</strong> 所有顶点都会被访问 <span class="arithmatex">\(1\)</span> 次，使用 <span class="arithmatex">\(O(|V|)\)</span> 时间；所有边都会被访问 <span class="arithmatex">\(2\)</span> 次，使用 <span class="arithmatex">\(O(2|E|)\)</span> 时间；总体使用 <span class="arithmatex">\(O(|V| + |E|)\)</span> 时间。</p>
<p><strong>空间复杂度：</strong> 列表 <code>res</code> ，哈希表 <code>visited</code> 顶点数量最多为 <span class="arithmatex">\(|V|\)</span> ，递归深度最大为 <span class="arithmatex">\(|V|\)</span> ，因此使用 <span class="arithmatex">\(O(|V|)\)</span> 空间。</p>
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