2493. Divide Nodes Into the Maximum Number of Groups
Problem Description
You have an undirected graph with n
nodes labeled from 1
to n
. The graph is defined by a list of edges where each edge [ai, bi]
represents a bidirectional connection between nodes ai
and bi
. Note that the graph may be disconnected (not all nodes are reachable from each other).
Your task is to divide all nodes into m
groups (numbered from 1
to m
) such that:
-
Every node belongs to exactly one group - Each of the
n
nodes must be assigned to one and only one group. -
Adjacent nodes must be in consecutive groups - If two nodes are connected by an edge, and one node is in group
x
while the other is in groupy
, then|y - x| = 1
. This means connected nodes must be placed in groups whose numbers differ by exactly 1.
The goal is to find the maximum number of groups (m
) that you can create while satisfying these conditions. If it's impossible to group the nodes according to these rules, return -1
.
For example, if you have a simple path graph with 4 nodes connected as 1-2-3-4
, you could assign:
- Node 1 to group 1
- Node 2 to group 2
- Node 3 to group 3
- Node 4 to group 4
This would give you a maximum of 4 groups. However, if the graph contains an odd-length cycle, it would be impossible to satisfy the constraints (similar to bipartite graph coloring), and you would return -1
.
Since the graph can be disconnected, you need to handle each connected component separately and sum up the maximum groups needed for each component.
Flowchart Walkthrough
First, let's pin down the algorithm using the Flowchart. Here's a step-by-step walkthrough:
Is it a graph?
- Yes: The problem explicitly states we have an undirected graph with
n
nodes and edges connecting them.
Is it a tree?
- No: The graph can have cycles (as mentioned in the solution, odd-length cycles would make grouping impossible), and it can also be disconnected. Trees are connected and acyclic.
Is the problem related to directed acyclic graphs?
- No: The graph is undirected, not a DAG.
Is the problem related to shortest paths?
- Yes: While not explicitly about shortest paths, the problem requires finding distances/levels from starting nodes. We need to assign groups based on distance from a chosen starting point, where adjacent nodes must differ by exactly 1 in their group numbers (similar to BFS levels).
Is the graph weighted?
- No: The edges don't have weights; all edges are equal.
BFS
- Yes: Since we need unweighted shortest paths/levels, BFS is the appropriate choice.
Conclusion: The flowchart suggests using BFS for this problem. While the title mentions DFS pattern, the actual solution uses BFS to traverse the graph level by level, assigning group numbers based on distance from starting nodes. The BFS approach naturally ensures that connected nodes are assigned to consecutive groups (differing by 1), which is exactly what the problem requires.
Note: The solution actually uses BFS (with a queue and level-wise traversal) rather than DFS, despite the initial request mentioning DFS pattern. The BFS approach is more suitable here because we need to process nodes level by level to ensure the consecutive group constraint is satisfied.
Intuition
The key insight is to think of this problem as a graph coloring problem with a special constraint: adjacent nodes must have colors (group numbers) that differ by exactly 1.
Let's build the intuition step by step:
-
Why BFS? When we assign group numbers starting from any node, we want adjacent nodes to have consecutive group numbers. This is exactly like finding distances in an unweighted graph - nodes at distance 1 get group 2, nodes at distance 2 get group 3, and so on. BFS naturally gives us these levels.
-
Why enumerate starting points? Different starting points in the same connected component can give different maximum depths. Consider a path graph
1-2-3
. If we start from node 2 (middle), we get 2 groups maximum. But if we start from node 1 (end), we get 3 groups. So we need to try all possible starting points to maximize the number of groups. -
How to detect impossibility? During BFS traversal, if we encounter a node that's already been visited and its assigned group number doesn't match what we expect (differ by 1 from its neighbor), then it's impossible to satisfy the constraints. This happens with odd-length cycles - imagine a triangle where node A is in group 1, B in group 2, C in group 3, but C must also be adjacent to A (group 1), requiring C to be in group 2, which is a contradiction.
-
Why track by connected components? Since the graph can be disconnected, each connected component can be grouped independently. The total number of groups is the sum of maximum groups from each component. We use the smallest node in each component as a representative to avoid counting the same component multiple times.
-
The overall strategy: For each node as a potential starting point, run BFS to assign group numbers based on distance. Track the maximum depth (number of groups) for each connected component. If at any point we find conflicting group assignments, return
-1
. Otherwise, sum up the maximum groups from all components.
This approach essentially finds the diameter (longest shortest path) for each connected component when valid, which gives us the maximum number of groups possible.
Learn more about Depth-First Search, Breadth-First Search, Union Find and Graph patterns.
Solution Approach
The solution implements BFS with enumeration to find the maximum number of groups for each connected component. Here's how the implementation works:
1. Build the Adjacency List
g = [[] for _ in range(n)]
for a, b in edges:
g[a - 1].append(b - 1)
g[b - 1].append(a - 1)
We convert the 1-indexed nodes to 0-indexed and create a bidirectional adjacency list representation of the graph.
2. Track Maximum Groups per Component
d = defaultdict(int)
We use a dictionary d
where the key is the root node (smallest node) of each connected component, and the value is the maximum number of groups for that component.
3. Enumerate Each Node as Starting Point
for i in range(n):
q = deque([i])
dist = [0] * n
dist[i] = mx = 1
root = i
For each node i
, we:
- Initialize a BFS queue with node
i
- Create a
dist
array to track the group number (distance + 1) for each node - Set the starting node's distance to 1 (first group)
- Initialize
mx
to track the maximum depth - Initialize
root
to track the smallest node in this component
4. BFS Traversal
while q:
a = q.popleft()
root = min(root, a)
for b in g[a]:
if dist[b] == 0:
dist[b] = dist[a] + 1
mx = max(mx, dist[b])
q.append(b)
elif abs(dist[b] - dist[a]) != 1:
return -1
During BFS:
- We continuously update
root
to be the minimum node encountered (to identify the component) - For unvisited neighbors (
dist[b] == 0
), we assign them to the next group (dist[a] + 1
) - For already visited neighbors, we verify the constraint: their group numbers must differ by exactly 1
- If the constraint is violated, we immediately return
-1
(impossible to group)
5. Update Component's Maximum
d[root] = max(d[root], mx)
After BFS from node i
, we update the maximum number of groups for this component. We use root
(smallest node in component) as the key to ensure all nodes in the same component update the same entry.
6. Sum All Components
return sum(d.values())
Finally, we sum the maximum groups from all connected components to get the total answer.
Time Complexity: O(n Γ (n + m))
where n
is the number of nodes and m
is the number of edges. We run BFS from each node, and each BFS takes O(n + m)
time.
Space Complexity: O(n + m)
for the adjacency list and auxiliary arrays.
Ready to land your dream job?
Unlock your dream job with a 5-minute evaluator for a personalized learning plan!
Start EvaluatorExample Walkthrough
Let's walk through a small example to illustrate the solution approach.
Example Graph:
- Nodes: 1, 2, 3, 4, 5
- Edges: [[1,2], [2,3], [3,4], [5,5]] (node 5 has a self-loop)
This creates two components:
- Component 1:
1-2-3-4
(a path) - Component 2:
5
(isolated with self-loop)
Step 1: Build Adjacency List After converting to 0-indexed:
g[0] = [1] // node 1 connects to node 2 g[1] = [0, 2] // node 2 connects to nodes 1 and 3 g[2] = [1, 3] // node 3 connects to nodes 2 and 4 g[3] = [2] // node 4 connects to node 3 g[4] = [4] // node 5 has self-loop
Step 2: Try Each Node as Starting Point
Starting from node 0 (node 1):
- BFS assigns: node 0βgroup 1, node 1βgroup 2, node 2βgroup 3, node 3βgroup 4
- Root (smallest in component) = 0
- Maximum groups = 4
- Update:
d[0] = 4
Starting from node 1 (node 2):
- BFS assigns: node 1βgroup 1, node 0βgroup 2, node 2βgroup 2, node 3βgroup 3
- When we visit node 2 from node 1, it gets group 2
- Later when checking edge 0-1: |2-2| = 0 β 1 β Conflict!
- Actually, let me recalculate: node 1βgroup 1, then node 0 gets group 2 and node 2 gets group 2
- When BFS processes node 0 (group 2), it checks its neighbor node 1 (group 1): |2-1| = 1 β
- When BFS processes node 2 (group 2), it continues to node 3 (group 3)
- Root = 0, Maximum groups = 3
- Update:
d[0] = max(4, 3) = 4
Starting from node 2 (node 3):
- BFS assigns: node 2βgroup 1, node 1βgroup 2, node 3βgroup 2
- Then node 0βgroup 3, node 3 is already visited
- Check edge 2-3: both nodes 1 and 3 have group 2, |2-2| = 0 β 1 β Wait, this is wrong
- Let me recalculate: node 2βgroup 1, its neighbors are nodes 1 and 3
- Node 1βgroup 2, node 3βgroup 2
- From node 1, we reach node 0βgroup 3
- From node 3, no new nodes
- When checking edge 2-3: |2-1| = 1 β
- Root = 0, Maximum groups = 3
- Update:
d[0] = max(4, 3) = 4
Starting from node 4 (node 5 with self-loop):
- BFS assigns: node 4βgroup 1
- Check self-loop: node 4 connects to itself
- Constraint check: |1-1| = 0 β 1 β Return -1
The self-loop violates the constraint that adjacent nodes must be in consecutive groups. A node cannot be in a group that differs by 1 from itself.
Result: Return -1
(impossible to group)
Alternative Example Without Self-Loop:
If we remove the self-loop and have edges [[1,2], [2,3], [3,4]]:
Starting from each node:
- From node 0: groups [1,2,3,4], max = 4, root = 0
- From node 1: groups [2,1,2,3], max = 3, root = 0
- From node 2: groups [3,2,1,2], max = 3, root = 0
- From node 3: groups [4,3,2,1], max = 4, root = 0
Final: d[0] = 4
, so return sum(d.values()) = 4
Solution Implementation
1class Solution:
2 def magnificentSets(self, n: int, edges: List[List[int]]) -> int:
3 # Build adjacency list for the graph (converting to 0-indexed)
4 adjacency_list = [[] for _ in range(n)]
5 for node_a, node_b in edges:
6 adjacency_list[node_a - 1].append(node_b - 1)
7 adjacency_list[node_b - 1].append(node_a - 1)
8
9 # Dictionary to store maximum depth for each connected component
10 # Key: root node (smallest node in component), Value: max depth
11 component_max_depths = defaultdict(int)
12
13 # Try BFS from each node to find maximum possible depth
14 for start_node in range(n):
15 # Initialize BFS queue and distance array
16 queue = deque([start_node])
17 distances = [0] * n # 0 means unvisited
18 distances[start_node] = 1 # Start with distance 1
19 max_distance = 1
20 component_root = start_node # Track smallest node in this BFS traversal
21
22 # Perform BFS traversal
23 while queue:
24 current_node = queue.popleft()
25 # Update component root to be the minimum node seen
26 component_root = min(component_root, current_node)
27
28 # Check all neighbors
29 for neighbor in adjacency_list[current_node]:
30 if distances[neighbor] == 0:
31 # Unvisited neighbor: assign distance and add to queue
32 distances[neighbor] = distances[current_node] + 1
33 max_distance = max(max_distance, distances[neighbor])
34 queue.append(neighbor)
35 elif abs(distances[neighbor] - distances[current_node]) != 1:
36 # Already visited neighbor: check if graph is bipartite
37 # Adjacent nodes must have consecutive distances
38 return -1
39
40 # Update maximum depth for this component's root
41 component_max_depths[component_root] = max(
42 component_max_depths[component_root],
43 max_distance
44 )
45
46 # Sum up maximum depths of all connected components
47 return sum(component_max_depths.values())
48
1class Solution {
2 public int magnificentSets(int n, int[][] edges) {
3 // Build adjacency list representation of the graph
4 List<Integer>[] adjacencyList = new List[n];
5 Arrays.setAll(adjacencyList, index -> new ArrayList<>());
6
7 // Populate the adjacency list (converting to 0-indexed)
8 for (int[] edge : edges) {
9 int nodeA = edge[0] - 1; // Convert from 1-indexed to 0-indexed
10 int nodeB = edge[1] - 1;
11 adjacencyList[nodeA].add(nodeB);
12 adjacencyList[nodeB].add(nodeA);
13 }
14
15 // Array to store the maximum depth for each connected component (indexed by component root)
16 int[] maxDepthByComponent = new int[n];
17
18 // Temporary array to store distances during BFS
19 int[] distances = new int[n];
20
21 // Try BFS from each node to find the maximum possible grouping
22 for (int startNode = 0; startNode < n; ++startNode) {
23 // Initialize BFS queue
24 Deque<Integer> queue = new ArrayDeque<>();
25 queue.offer(startNode);
26
27 // Reset distances array for this BFS
28 Arrays.fill(distances, 0);
29 distances[startNode] = 1; // Starting node is at level 1
30
31 int maxDistance = 1;
32 int componentRoot = startNode; // Track the minimum node ID in this component
33
34 // Perform BFS to explore the connected component
35 while (!queue.isEmpty()) {
36 int currentNode = queue.poll();
37
38 // Update component root to be the minimum node ID seen
39 componentRoot = Math.min(componentRoot, currentNode);
40
41 // Process all neighbors
42 for (int neighbor : adjacencyList[currentNode]) {
43 if (distances[neighbor] == 0) {
44 // Unvisited neighbor - assign it to the next level
45 distances[neighbor] = distances[currentNode] + 1;
46 maxDistance = Math.max(maxDistance, distances[neighbor]);
47 queue.offer(neighbor);
48 } else if (Math.abs(distances[neighbor] - distances[currentNode]) != 1) {
49 // Adjacent nodes must be in consecutive levels
50 // If not, the graph cannot be properly partitioned
51 return -1;
52 }
53 }
54 }
55
56 // Update the maximum depth for this component (using component root as key)
57 maxDepthByComponent[componentRoot] = Math.max(maxDepthByComponent[componentRoot], maxDistance);
58 }
59
60 // Sum up the maximum depths of all components
61 return Arrays.stream(maxDepthByComponent).sum();
62 }
63}
64
1class Solution {
2public:
3 int magnificentSets(int n, vector<vector<int>>& edges) {
4 // Build adjacency list representation of the graph
5 vector<vector<int>> adjacencyList(n);
6 for (const auto& edge : edges) {
7 int nodeA = edge[0] - 1; // Convert to 0-indexed
8 int nodeB = edge[1] - 1; // Convert to 0-indexed
9 adjacencyList[nodeA].push_back(nodeB);
10 adjacencyList[nodeB].push_back(nodeA);
11 }
12
13 // Store the maximum depth for each connected component
14 // Using the minimum node index as the component representative
15 vector<int> componentMaxDepth(n, 0);
16
17 // Try BFS from each node to find the maximum possible depth
18 for (int startNode = 0; startNode < n; ++startNode) {
19 queue<int> bfsQueue;
20 bfsQueue.push(startNode);
21
22 // Track distance from start node (1-indexed to indicate visited)
23 vector<int> distance(n, 0);
24 distance[startNode] = 1;
25
26 int maxDistance = 1;
27 int componentRoot = startNode; // Track minimum node in this component
28
29 // Perform BFS to assign distances and check for bipartite property
30 while (!bfsQueue.empty()) {
31 int currentNode = bfsQueue.front();
32 bfsQueue.pop();
33
34 // Update component root to be the minimum node index
35 componentRoot = min(componentRoot, currentNode);
36
37 // Process all neighbors
38 for (int neighbor : adjacencyList[currentNode]) {
39 if (distance[neighbor] == 0) {
40 // Unvisited neighbor - assign distance
41 distance[neighbor] = distance[currentNode] + 1;
42 maxDistance = max(maxDistance, distance[neighbor]);
43 bfsQueue.push(neighbor);
44 } else if (abs(distance[neighbor] - distance[currentNode]) != 1) {
45 // Adjacent nodes must have distances differing by exactly 1
46 // If not, the graph cannot be properly leveled
47 return -1;
48 }
49 }
50 }
51
52 // Update the maximum depth for this component
53 // Use the minimum node as the component identifier
54 componentMaxDepth[componentRoot] = max(componentMaxDepth[componentRoot], maxDistance);
55 }
56
57 // Sum up the maximum depths of all components
58 return accumulate(componentMaxDepth.begin(), componentMaxDepth.end(), 0);
59 }
60};
61
1/**
2 * Finds the maximum number of groups that can be formed from a graph
3 * where each node must be assigned to a group such that adjacent nodes
4 * have consecutive group numbers.
5 *
6 * @param n - The number of nodes in the graph (1-indexed)
7 * @param edges - Array of edges where each edge is [nodeA, nodeB]
8 * @returns The sum of maximum depths for each connected component, or -1 if impossible
9 */
10function magnificentSets(n: number, edges: number[][]): number {
11 // Build adjacency list representation of the graph (0-indexed)
12 const adjacencyList: number[][] = Array.from({ length: n }, () => []);
13 for (const [nodeA, nodeB] of edges) {
14 adjacencyList[nodeA - 1].push(nodeB - 1); // Convert to 0-indexed
15 adjacencyList[nodeB - 1].push(nodeA - 1);
16 }
17
18 // Store the maximum depth for each connected component (indexed by root)
19 const maxDepthByComponent: number[] = Array(n).fill(0);
20
21 // Try BFS from each node to find the maximum possible depth
22 for (let startNode = 0; startNode < n; ++startNode) {
23 const queue: number[] = [startNode];
24 const distances: number[] = Array(n).fill(0);
25 distances[startNode] = 1; // Starting node is at level 1
26 let maxDistance: number = 1;
27 let componentRoot: number = startNode; // Track smallest node in component
28
29 // BFS to assign levels and check bipartite-like property
30 while (queue.length > 0) {
31 const currentNode: number = queue.shift()!;
32 componentRoot = Math.min(componentRoot, currentNode);
33
34 for (const neighbor of adjacencyList[currentNode]) {
35 if (distances[neighbor] === 0) {
36 // Unvisited neighbor: assign next level
37 distances[neighbor] = distances[currentNode] + 1;
38 maxDistance = Math.max(maxDistance, distances[neighbor]);
39 queue.push(neighbor);
40 } else if (Math.abs(distances[neighbor] - distances[currentNode]) !== 1) {
41 // Adjacent nodes must have consecutive levels
42 // If not, the graph cannot be properly grouped
43 return -1;
44 }
45 }
46 }
47
48 // Update maximum depth for this component (identified by smallest node)
49 maxDepthByComponent[componentRoot] = Math.max(maxDepthByComponent[componentRoot], maxDistance);
50 }
51
52 // Sum up the maximum depths of all connected components
53 return maxDepthByComponent.reduce((sum, depth) => sum + depth, 0);
54}
55
Time and Space Complexity
Time Complexity: O(n Γ (n + m))
The algorithm performs a BFS from each of the n
nodes. For each starting node i
:
- The BFS traverses all reachable nodes in the connected component, visiting each node once
- For each visited node, we check all its adjacent edges
- In the worst case, a connected component can have all
n
nodes and allm
edges
Since we run this BFS for each of the n
starting positions, and each BFS takes O(n + m)
time (visiting at most n
nodes and traversing at most m
edges), the total time complexity is O(n Γ (n + m))
.
Space Complexity: O(n + m)
The space usage consists of:
- Graph adjacency list
g
: stores all edges, requiringO(n + m)
space (array of sizen
plus all edge connections) - Queue
q
for BFS: at mostO(n)
nodes can be in the queue - Distance array
dist
:O(n)
space for tracking distances - Dictionary
d
: stores at mostn
entries (one per connected component root), requiringO(n)
space
The dominant space usage is the graph representation at O(n + m)
, while all other data structures use O(n)
space. Therefore, the overall space complexity is O(n + m)
.
Learn more about how to find time and space complexity quickly.
Common Pitfalls
1. Incorrectly Identifying Connected Components
The Pitfall:
The current solution uses component_root = min(component_root, current_node)
during BFS to identify components. However, this approach has a critical flaw: different BFS starting points within the same component might discover different subsets of nodes, leading to different "minimum nodes" being identified as the root. This causes the same component to be counted multiple times with different keys in the dictionary.
Example Scenario:
Consider a graph: 1-2-3
where nodes form a path.
- BFS from node 1: visits all nodes,
component_root = 1
- BFS from node 3: visits all nodes,
component_root = 1
- BFS from node 2: visits all nodes,
component_root = 1
But if we had a more complex graph structure, the BFS traversal order could lead to different minimum nodes being discovered from different starting points.
The Solution: Pre-compute connected components using a separate DFS/BFS or Union-Find, ensuring each node knows which component it belongs to:
def magnificentSets(self, n: int, edges: List[List[int]]) -> int:
# Build adjacency list
adjacency_list = [[] for _ in range(n)]
for node_a, node_b in edges:
adjacency_list[node_a - 1].append(node_b - 1)
adjacency_list[node_b - 1].append(node_a - 1)
# First, identify all connected components
component_id = [-1] * n
current_component = 0
for i in range(n):
if component_id[i] == -1:
# DFS to mark all nodes in this component
stack = [i]
while stack:
node = stack.pop()
if component_id[node] == -1:
component_id[node] = current_component
for neighbor in adjacency_list[node]:
if component_id[neighbor] == -1:
stack.append(neighbor)
current_component += 1
# Now find maximum depth for each component
component_max_depths = defaultdict(int)
for start_node in range(n):
queue = deque([start_node])
distances = [0] * n
distances[start_node] = 1
max_distance = 1
while queue:
current_node = queue.popleft()
for neighbor in adjacency_list[current_node]:
if distances[neighbor] == 0:
distances[neighbor] = distances[current_node] + 1
max_distance = max(max_distance, distances[neighbor])
queue.append(neighbor)
elif abs(distances[neighbor] - distances[current_node]) != 1:
return -1
# Use the pre-computed component ID instead of tracking minimum node
comp_id = component_id[start_node]
component_max_depths[comp_id] = max(
component_max_depths[comp_id],
max_distance
)
return sum(component_max_depths.values())
2. Not Checking for Odd Cycles Early
The Pitfall: The current solution checks for bipartiteness (odd cycles) during each BFS traversal. This means we might perform many redundant BFS traversals before discovering that the graph contains an odd cycle, wasting computational resources.
The Solution: Check bipartiteness once for each component before attempting to find the maximum depth:
def is_bipartite_component(start, adjacency_list, visited):
color = {}
queue = deque([start])
color[start] = 0
while queue:
node = queue.popleft()
visited[node] = True
for neighbor in adjacency_list[node]:
if neighbor not in color:
color[neighbor] = 1 - color[node]
queue.append(neighbor)
elif color[neighbor] == color[node]:
return False
return True
# Check bipartiteness for all components first
visited = [False] * n
for i in range(n):
if not visited[i]:
if not is_bipartite_component(i, adjacency_list, visited):
return -1
This optimization ensures we fail fast if the graph cannot be properly grouped, avoiding unnecessary BFS traversals from every node.
Which algorithm should you use to find a node that is close to the root of the tree?
Recommended Readings
https assets algo monster cover_photos dfs svg Depth First Search Prereqs Recursion Review problems recursion_intro Trees problems tree_intro With a solid understanding of recursion under our belts we are now ready to tackle one of the most useful techniques in coding interviews Depth First Search DFS As the name suggests
https assets algo monster cover_photos bfs svg Breadth First Search on Trees Hopefully by this time you've drunk enough DFS Kool Aid to understand its immense power and seen enough visualization to create a call stack in your mind Now let me introduce the companion spell Breadth First Search BFS
Union Find Disjoint Set Union Data Structure Introduction Prerequisite Depth First Search Review problems dfs_intro Once we have a strong grasp of recursion and Depth First Search we can now introduce Disjoint Set Union DSU This data structure is motivated by the following problem Suppose we have sets of elements
Want a Structured Path to Master System Design Too? Donβt Miss This!