Description:

Given a reference of a node in a connected undirected graph, return a deep copy (clone) of the graph. Each node in the graph contains a val (int) and a list (List[Node]) of its neighbors.

Example:

Input:
{"id":"2","neighbors":[{"id":"3","neighbors":[{"id":"4","neighbors":[{"ref":"1"}],"val":4}],"val":3}],"val":2},{"$ref":"4"}],"val":1}

Explanation:
Node 1's value is 1, and it has two neighbors: Node 2 and 4.
Node 2's value is 2, and it has two neighbors: Node 1 and 3.
Node 3's value is 3, and it has two neighbors: Node 2 and 4.
Node 4's value is 4, and it has two neighbors: Node 1 and 3.

Note:

1. The number of nodes will be between 1 and 100.
2. The undirected graph is a simple graph, which means no repeated edges and no self-loops in the graph.
3. Since the graph is undirected, if node p has node q as neighbor, then node q must have node p as neighbor too.
4. You must return the copy of the given node as a reference to the cloned graph.

Solution:

NOTE: BFS遍历graph，然后duplicate每一层的node。每一层内，挨个找下一层的节点（next_），遇到没有visited，新建一个新的node，把老node和新node放到hashtable（叫visited）里建立映射关系（给层内node之间的链接提供帮助），遇到遇见过的next_调用hashtable找到那个对应的新node。
中间没读懂题意，看到Node定义需要双变量输入，以为必须是offline建立图，卡了一会。

"""
# Definition for a Node.
class Node:
    def __init__(self, val, neighbors):
        self.val = val
        self.neighbors = neighbors
"""
class Solution:
    def cloneGraph(self, node: 'Node') -> 'Node':
        
        node_ls = [node]
        ret = Node(node.val,[])
        visited = {node:ret}
        node_ls2 = [ret]
        
        def bfs(node_ls,node_ls2):
            nonlocal visited
            
            next_layer = []
            next_layer2 = []
            for i,n in enumerate(node_ls):
                for next_ in n.neighbors:
                    if next_ not in visited:
                        next_layer2.append(Node(next_.val,[]))
                        next_layer.append(next_)
                        visited[next_] = next_layer2[-1]
                    node_ls2[i].neighbors.append(visited[next_])
                    
            if next_layer:
                bfs(next_layer,next_layer2)
                
        bfs(node_ls,node_ls2)
        return ret

Runtime: 40 ms, faster than 96.53% of Python3 online submissions for Clone Graph.
Memory Usage: 14.4 MB, less than 5.70% of Python3 online submissions for Clone Graph.