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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4v-2m10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1 2.1 2.1Z"/></svg>
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<h1 id="91">9.1. &nbsp;<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">\[
\begin{aligned}
V &amp; = \{ 1, 2, 3, 4, 5 \} \newline
E &amp; = \{ (1,2), (1,3), (1,5), (2,3), (2,4), (2,5), (4,5) \} \newline
G &amp; = \{ V, E \} \newline
\end{aligned}
\]</div>
<p><img alt="链表、树、图之间的关系" src="../graph.assets/linkedlist_tree_graph.png" /></p>
<p align="center"> Fig. 链表、树、图之间的关系 </p>
<p>那么,图与其他数据结构的关系是什么?如果我们把「顶点」看作节点,把「边」看作连接各个节点的指针,则可将「图」看作是一种从「链表」拓展而来的数据结构。<strong>相较于线性关系(链表)和分治关系(树),网络关系(图)的自由度更高,从而更为复杂</strong></p>
<h2 id="911">9.1.1. &nbsp; 图常见类型<a class="headerlink" href="#911" title="Permanent link">&para;</a></h2>
<p>根据边是否具有方向,可分为「无向图 Undirected Graph」和「有向图 Directed Graph」。</p>
<ul>
<li>在无向图中,边表示两顶点之间的“双向”连接关系,例如微信或 QQ 中的“好友关系”;</li>
<li>在有向图中,边具有方向性,即 <span class="arithmatex">\(A \rightarrow B\)</span><span class="arithmatex">\(A \leftarrow B\)</span> 两个方向的边是相互独立的,例如微博或抖音上的“关注”与“被关注”关系;</li>
</ul>
<p><img alt="有向图与无向图" src="../graph.assets/directed_graph.png" /></p>
<p align="center"> Fig. 有向图与无向图 </p>
<p>根据所有顶点是否连通,可分为「连通图 Connected Graph」和「非连通图 Disconnected Graph」。</p>
<ul>
<li>对于连通图,从某个顶点出发,可以到达其余任意顶点;</li>
<li>对于非连通图,从某个顶点出发,至少有一个顶点无法到达;</li>
</ul>
<p><img alt="连通图与非连通图" src="../graph.assets/connected_graph.png" /></p>
<p align="center"> Fig. 连通图与非连通图 </p>
<p>我们还可以为边添加“权重”变量,从而得到「有权图 Weighted Graph」。例如在王者荣耀等手游中系统会根据共同游戏时间来计算玩家之间的“亲密度”这种亲密度网络就可以用有权图来表示。</p>
<p><img alt="有权图与无权图" src="../graph.assets/weighted_graph.png" /></p>
<p align="center"> Fig. 有权图与无权图 </p>
<h2 id="912">9.1.2. &nbsp; 图常用术语<a class="headerlink" href="#912" title="Permanent link">&para;</a></h2>
<ul>
<li>「邻接 Adjacency」当两顶点之间存在边相连时称这两顶点“邻接”。在上图中顶点 1 的邻接顶点为顶点 2、3、5。</li>
<li>「路径 Path」从顶点 A 到顶点 B 经过的边构成的序列被称为从 A 到 B 的“路径”。在上图中,边序列 1-5-2-4 是顶点 1 到顶点 4 的一条路径。</li>
<li>「度 Degree」表示一个顶点拥有的边数。对于有向图「入度 In-Degree」表示有多少条边指向该顶点「出度 Out-Degree」表示有多少条边从该顶点指出。</li>
</ul>
<h2 id="913">9.1.3. &nbsp; 图的表示<a class="headerlink" href="#913" title="Permanent link">&para;</a></h2>
<p>图的常用表示方法包括「邻接矩阵」和「邻接表」。以下使用无向图进行举例。</p>
<h3 id="_1">邻接矩阵<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h3>
<p>设图的顶点数量为 <span class="arithmatex">\(n\)</span> ,「邻接矩阵 Adjacency Matrix」使用一个 <span class="arithmatex">\(n \times n\)</span> 大小的矩阵来表示图,每一行(列)代表一个顶点,矩阵元素代表边,用 <span class="arithmatex">\(1\)</span><span class="arithmatex">\(0\)</span> 表示两个顶点之间是否存在边。</p>
<p>如下图所示,设邻接矩阵为 <span class="arithmatex">\(M\)</span> 、顶点列表为 <span class="arithmatex">\(V\)</span> ,那么矩阵元素 <span class="arithmatex">\(M[i][j] = 1\)</span> 表示顶点 <span class="arithmatex">\(V[i]\)</span> 到顶点 <span class="arithmatex">\(V[j]\)</span> 之间存在边,反之 <span class="arithmatex">\(M[i][j] = 0\)</span> 表示两顶点之间无边。</p>
<p><img alt="图的邻接矩阵表示" src="../graph.assets/adjacency_matrix.png" /></p>
<p align="center"> Fig. 图的邻接矩阵表示 </p>
<p>邻接矩阵具有以下特性:</p>
<ul>
<li>顶点不能与自身相连,因此邻接矩阵主对角线元素没有意义。</li>
<li>对于无向图,两个方向的边等价,此时邻接矩阵关于主对角线对称。</li>
<li>将邻接矩阵的元素从 <span class="arithmatex">\(1\)</span> , <span class="arithmatex">\(0\)</span> 替换为权重,则可表示有权图。</li>
</ul>
<p>使用邻接矩阵表示图时,我们可以直接访问矩阵元素以获取边,因此增删查操作的效率很高,时间复杂度均为 <span class="arithmatex">\(O(1)\)</span> 。然而,矩阵的空间复杂度为 <span class="arithmatex">\(O(n^2)\)</span> ,内存占用较多。</p>
<h3 id="_2">邻接表<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h3>
<p>「邻接表 Adjacency List」使用 <span class="arithmatex">\(n\)</span> 个链表来表示图,链表节点表示顶点。第 <span class="arithmatex">\(i\)</span> 条链表对应顶点 <span class="arithmatex">\(i\)</span> ,其中存储了该顶点的所有邻接顶点(即与该顶点相连的顶点)。</p>
<p><img alt="图的邻接表表示" src="../graph.assets/adjacency_list.png" /></p>
<p align="center"> Fig. 图的邻接表表示 </p>
<p>邻接表仅存储实际存在的边,而边的总数通常远小于 <span class="arithmatex">\(n^2\)</span> ,因此它更加节省空间。然而,在邻接表中需要通过遍历链表来查找边,因此其时间效率不如邻接矩阵。</p>
<p>观察上图可发现,<strong>邻接表结构与哈希表中的「链地址法」非常相似,因此我们也可以采用类似方法来优化效率</strong>。例如,当链表较长时,可以将链表转化为 AVL 树或红黑树,从而将时间效率从 <span class="arithmatex">\(O(n)\)</span> 优化至 <span class="arithmatex">\(O(\log n)\)</span> ,还可以通过中序遍历获取有序序列;此外,还可以将链表转换为哈希表,将时间复杂度降低至 <span class="arithmatex">\(O(1)\)</span></p>
<h2 id="914">9.1.4. &nbsp; 图常见应用<a class="headerlink" href="#914" title="Permanent link">&para;</a></h2>
<p>实际应用中,许多系统都可以用图来建模,相应的待求解问题也可以约化为图计算问题。</p>
<div class="center-table">
<table>
<thead>
<tr>
<th></th>
<th>顶点</th>
<th></th>
<th>图计算问题</th>
</tr>
</thead>
<tbody>
<tr>
<td>社交网络</td>
<td>用户</td>
<td>好友关系</td>
<td>潜在好友推荐</td>
</tr>
<tr>
<td>地铁线路</td>
<td>站点</td>
<td>站点间的连通性</td>
<td>最短路线推荐</td>
</tr>
<tr>
<td>太阳系</td>
<td>星体</td>
<td>星体间的万有引力作用</td>
<td>行星轨道计算</td>
</tr>
</tbody>
</table>
</div>
<h2 id="__comments">评论</h2>
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