Clone graph#

Levels: level-3

Data structures: graph

Algorithms: dfs, bfs

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.

Test case format:

For simplicity sake, each node’s value is the same as the node’s index (1-indexed). For example, the first node with val = 1, the second node with val = 2, and so on. The graph is represented in the test case using an adjacency list.
Adjacency list is a collection of unordered lists used to represent a finite graph. Each list describes the set of neighbors of a node in the graph.
The given node will always be the first node with val = 1. You must return the copy of the given node as a reference to the cloned graph.

Constraints:

1 <= Node.val <= 100
Node.val is unique for each node.
Number of Nodes will not exceed 100.
There is no repeated edges and no self-loops in the graph.
The Graph is connected and all nodes can be visited starting from the given node.

Examples#

1Input: adjList = [[2,4],[1,3],[2,4],[1,3]]
2Output: [[2,4],[1,3],[2,4],[1,3]]
3Explanation: There are 4 nodes in the graph.
41st node (val = 1)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).
52nd node (val = 2)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).
63rd node (val = 3)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).
74th node (val = 4)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).

1Input: adjList = [[]]
2Output: [[]]
3Explanation: Note that the input contains one empty list. The graph consists of only one node with val = 1 and it does not have any neighbors.

Python Solution#

 1class Node:
 2    def __init__(self, val=0, neighbors=[]):
 3        self.val = val
 4        self.neighbors = neighbors
 5
 6
 7class Solution:
 8    def cloneGraph(self, node: 'Node') -> 'Node':
 9        dic = {}
10
11        def dfs(node):
12            if not node:
13                return
14            else:
15                node_copy = Node(node.val, [])
16                dic[node] = node_copy
17                for nei in node.neighbors:
18                    if nei in dic:
19                        node_copy.neighbors.append(dic[nei])
20                    else:
21                        node_copy.neighbors.append(dfs(nei))
22                return node_copy
23
24        return dfs(node)