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概述：
最近要学习写网络爬虫，所以把图的深度和广度搜索都再温习一下。
图结构展示：
实现过程：
首先，我们来看看图结构在代码中的实现。有三块逻辑：
1.图中的节点：
-
public
class GraphNode {
-
-
public List<GraphEdge> edgeList =
null;
-
-
private String label =
"";
-
-
public GraphNode(String label) {
-
this.label = label;
-
if (edgeList ==
null) {
-
edgeList =
new ArrayList<GraphEdge>();
-
}
-
}
-
-
/**
-
* 给当前节点添加一条边
-
* GraphNode
-
* @param edge
-
* 添加的边
-
*/
-
public void addEdgeList(GraphEdge edge) {
-
edgeList.add(edge);
-
}
-
-
public String getLabel() {
-
return label;
-
}
-
}
2.图中的边：
-
public
class GraphEdge {
-
-
private GraphNode nodeLeft;
-
-
private GraphNode nodeRight;
-
-
/**
-
* @param nodeLeft
-
* 边的左端
-
* @param nodeRight
-
* 边的右端
-
*/
-
public GraphEdge(GraphNode nodeLeft, GraphNode nodeRight) {
-
this.nodeLeft = nodeLeft;
-
this.nodeRight = nodeRight;
-
}
-
-
public GraphNode getNodeLeft() {
-
return nodeLeft;
-
}
-
-
public GraphNode getNodeRight() {
-
return nodeRight;
-
}
-
-
}
3.把节点和边组合成一个图结构：
-
public
class MyGraph {
-
-
private List<GraphNode> nodes =
null;
-
-
public void initGraph(int n) {
-
if (nodes ==
null) {
-
nodes =
new ArrayList<GraphNode>();
-
}
-
-
GraphNode node =
null;
-
for (
int i =
0; i < n; i++) {
-
node =
new GraphNode(String.valueOf(i));
-
nodes.add(node);
-
}
-
}
-
-
public void initGraph(int n, boolean b) {
-
initGraph(n);
-
GraphEdge edge01 =
new GraphEdge(nodes.get(
0), nodes.get(
1));
-
GraphEdge edge02 =
new GraphEdge(nodes.get(
0), nodes.get(
2));
-
GraphEdge edge13 =
new GraphEdge(nodes.get(
1), nodes.get(
3));
-
GraphEdge edge14 =
new GraphEdge(nodes.get(
1), nodes.get(
4));
-
GraphEdge edge25 =
new GraphEdge(nodes.get(
2), nodes.get(
5));
-
GraphEdge edge26 =
new GraphEdge(nodes.get(
2), nodes.get(
6));
-
GraphEdge edge37 =
new GraphEdge(nodes.get(
3), nodes.get(
7));
-
GraphEdge edge47 =
new GraphEdge(nodes.get(
4), nodes.get(
7));
-
GraphEdge edge56 =
new GraphEdge(nodes.get(
5), nodes.get(
6));
-
-
-
nodes.get(
0).addEdgeList(edge01);
-
nodes.get(
0).addEdgeList(edge02);
-
nodes.get(
1).addEdgeList(edge13);
-
nodes.get(
1).addEdgeList(edge14);
-
nodes.get(
2).addEdgeList(edge25);
-
nodes.get(
2).addEdgeList(edge26);
-
nodes.get(
3).addEdgeList(edge37);
-
nodes.get(
4).addEdgeList(edge47);
-
nodes.get(
5).addEdgeList(edge56);
-
}
-
-
public void initGraph() {
-
initGraph(
8,
false);
-
}
-
-
public List<GraphNode> getGraphNodes() {
-
return nodes;
-
}
-
}
有了图的结构，我们就可以进行一些实际的操作了。
深度优先搜索：
-
public
class DFSearch {
-
-
/**
-
* 深度遍历
-
* DFSearch
-
* @param node
-
* 当前节点
-
* @param visited
-
* 被访问过的节点列表
-
*/
-
public void searchTraversing(GraphNode node, List<GraphNode> visited) {
-
// 判断是否遍历过
-
if (visited.contains(node)) {
-
return;
-
}
-
-
visited.add(node);
-
System.out.println(
"节点：" + node.getLabel());
-
for (
int i =
0; i < node.edgeList.size(); i++) {
-
searchTraversing(node.edgeList.get(i).getNodeRight(), visited);
-
}
-
}
-
}
广度优先搜索：
-
public
class BFSearch {
-
-
/**
-
* 广度优先搜索
-
* BFSearch
-
* @param node
-
* 搜索的入口节点
-
*/
-
public void searchTraversing(GraphNode node) {
-
List<GraphNode> visited =
new ArrayList<GraphNode>();
// 已经被访问过的元素
-
Queue<GraphNode> q =
new LinkedList<GraphNode>();
// 用队列存放依次要遍历的元素
-
q.offer(node);
-
-
while (!q.isEmpty()) {
-
GraphNode currNode = q.poll();
-
if (!visited.contains(currNode)) {
-
visited.add(currNode);
-
System.out.println(
"节点：" + currNode.getLabel());
-
for (
int i =
0; i < currNode.edgeList.size(); i++) {
-
q.offer(currNode.edgeList.get(i).getNodeRight());
-
}
-
}
-
}
-
}
-
}
运行结果：
源码下载：
http://download.youkuaiyun.com/detail/u013761665/8968443
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