# BFS
graph = {
    "A" : ["B", "C"],
    "B" : ["A", "C", "D"],
    "C" : ["A", "B", "D", "E"],
    "D" : ["B", "C", "E", "F"],
    "E" : ["C", "D"],
    "F" : ["D"]
}

def BFS(graph, startNode):
    queue = []
    queue.append(startNode)
    seen = set()
    seen.add(startNode)

    while (len(queue)
 
 > 0):
        vertex = queue.pop(0)
        print(vertex)
        nodes = graph[vertex]
        for w in nodes:
            if w not in seen:
                queue.append(w)
                seen.add(w)

BFS(graph, "A")

BFS 使用佇列控制遍歷路徑，隊首彈出當前需要訪問的結點，隊尾插入與當前結點相連的結點。

# DFS
graph = {
    "A" : ["B", "C"
 
],
    "B" : ["A", "C", "D"],
    "C" : ["A", "B", "D", "E"],
    "D" : ["B", "C", "E", "F"],
    "E" : ["C", "D"],
    "F" : ["D"]
}

def DFS(graph, startNode):
    stack = []
    stack.append(startNode)
    seen = set()
    seen.add(startNode)

    while (len(stack) > 0):
        vertex = stack.
 
pop()
        print(vertex)
        nodes = graph[vertex]
        for w in nodes:
            if w not in seen:
                stack.append(w)
                seen.add(w)

DFS(graph, "A")

DFS 使用堆疊控制遍歷（訪問）路徑。

仔細觀察可以發現在由 BFS 改寫成 DFS 的過程中，只需要改動一個地方即可，pop(0) 是 BFS，pop()就是 DFS。 甚至都不用將 queue 改成 stack。Amazing！！！ (^-)