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, s):
    queue = []
    queue.append(s) # 向list添加元素，用append()
    seen = set() # 此处为set, python里set用的是hash table, 搜索时比数组要快。
    seen.add(s) # 向set添加函数，用add()
    while (len(queue) > 0):
        vertex = queue.pop(0)  #提取队头
        nodes = graph[vertex]  #获得队头元素的邻接元素
        for w in nodes:
            if w not in seen:
                queue.append(w) #将没有遍历过的子节点入队
                seen.add(w) #标记好已遍历
        print("当前出队的是：",vertex)

BFS(graph, ‘A‘)

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, s):
    stack=[]
    stack.append(s) # 向list添加元素，用append()
    seen = set() # 此处为set, python里set用的是hash table, 搜索时比数组要快。
    seen.add(s) # 向set添加函数，用add()
    while (len(stack) > 0):
        vertex = stack.pop() # 弹出最后一个元素
        nodes = graph[vertex]
        for w in nodes:
            if w not in seen:
                stack.append(w)
                seen.add(w)
        print("当前出栈的是",vertex)
DFS(graph, ‘A‘)