Maximum Number of K-Divisible Components

Updated on 10 June, 2025
Maximum Number of K-Divisible Components header image

Problem Statement

Consider a problem involving an undirected tree structure, consisting of n nodes uniquely labeled from 0 to n-1. The tree's structure is defined by a series of n-1 edges (each represented as a pair of nodes [ai, bi]), indicating the direct connectivity between nodes. Alongside this structure, each node i is associated with an integer value from a 0-indexed array values[i]. Additionally, an integer k is provided, serving a particular role in defining valid splits within the tree.

A valid split of this tree is achieved by selectively removing any set of edges (including the option of not removing any edges at all). The resulting sub-trees or components, post any splits, must each have total node values summing up to multiples of k. Here, the value of a component is calculated as the sum of values of its individual nodes.

The objective is to identify and execute such splits such that the total count of resulting sub-tree components (that adhere to the 'sum divisible by k' condition) is maximized. The output should be this maximum count of components.

Examples

Example 1

Input:

n = 5, edges = [[0,2],[1,2],[1,3],[2,4]], values = [1,8,1,4,4], k = 6

Output:

2

Explanation:

We remove the edge connecting node 1 with 2. The resulting split is valid because:
- The value of the component containing nodes 1 and 3 is values[1] + values[3] = 12.
- The value of the component containing nodes 0, 2, and 4 is values[0] + values[2] + values[4] = 6.
It can be shown that no other valid split has more than 2 connected components.

Example 2

Input:

n = 7, edges = [[0,1],[0,2],[1,3],[1,4],[2,5],[2,6]], values = [3,0,6,1,5,2,1], k = 3

Output:

3

Explanation:

We remove the edge connecting node 0 with 2, and the edge connecting node 0 with 1. The resulting split is valid because:
- The value of the component containing node 0 is values[0] = 3.
- The value of the component containing nodes 2, 5, and 6 is values[2] + values[5] + values[6] = 9.
- The value of the component containing nodes 1, 3, and 4 is values[1] + values[3] + values[4] = 6.
It can be shown that no other valid split has more than 3 connected components.

Constraints

  • 1 <= n <= 3 * 104
  • edges.length == n - 1
  • edges[i].length == 2
  • 0 <= ai, bi < n
  • values.length == n
  • 0 <= values[i] <= 109
  • 1 <= k <= 109
  • Sum of values is divisible by k.
  • The input is generated such that edges represents a valid tree.

Approach and Intuition

To solve this problem effectively, considering the provided examples and constraints:

  1. Understanding Tree Structure and Components:

    • Each component results from either splitting or not splitting edges in the initial tree.
    • Components are valid if their summed values are divisible by k.
  2. Decomposition of Problem:

    • Think in terms of starting at an arbitrary node and using a DFS (Depth-First Search) to navigate through the tree.
    • During the traversal, calculate and maintain the sum of values for each subtree.
  3. Evaluating Split Points:

    • As you calculate subtree sums during the DFS traversal, assess if the subtree sum so far is divisible by k.
    • If divisible at any node (excluding the case of the full tree's sum itself), consider this a split point where you might 'cut' the edge connecting this subtree to the rest of the tree.
  4. Maximization Strategy:

    • For each valid subtree (whose value sum % k == 0), consider it a potential separate component.
    • Use a recursive or iterative approach to keep track of the count of such valid components.
  5. Edge Cases and Constraints Handling:

    • Remember that single nodes can also be components if their value is divisible by k.
    • Implement checks where the total values along with the modular arithmetic properties (Sum of values is divisible by k) are utilized for quick invalidation of impossible configurations.
    • Ensure that the proposed solutions remain efficient within the bounds (up to 30,000 nodes).

The core of the solution involves comprehending and utilizing tree properties with strategic depth-first searches combined with value checks at each node leading to possible componential splits.

Solutions

  • C++
  • Java
  • Python
cpp
class Solution {
public:
    int maxDivisibleGroups(int nodes, vector<vector<int>>& connections,
                           vector<int>& nodeValues, int divisor) {
        if (nodes < 2) return 1;

        int divisibleCount = 0;
        vector<vector<int>> adjacencyList(nodes);
        vector<int> incomingEdges(nodes, 0);

        // Construct adjacency list and track incoming edge count for each node
        for (const auto& connection : connections) {
            int u = connection[0], v = connection[1];
            adjacencyList[u].push_back(v);
            adjacencyList[v].push_back(u);
            incomingEdges[u]++;
            incomingEdges[v]++;
        }

        // Use 64-bit integers to avoid overflow issues
        vector<long long> extendedValues(nodeValues.begin(), nodeValues.end());

        // Queue for BFS, starting with leaf nodes
        queue<int> processingQueue;
        for (int i = 0; i < nodes; i++) {
            if (incomingEdges[i] == 1) {
                processingQueue.push(i);
            }
        }

        // BFS with processing of values and in-degrees
        while (!processingQueue.empty()) {
            int current = processingQueue.front();
            processingQueue.pop();
            incomingEdges[current]--;

            long long contribution = 0;

            // Check divisibility condition for the current node
            if (extendedValues[current] % divisor == 0) {
                divisibleCount++;
            } else {
                contribution = extendedValues[current];
            }

            // Update neighboring nodes
            for (int neighbor : adjacencyList[current]) {
                if (incomingEdges[neighbor] == 0) {
                    continue;
                }

                incomingEdges[neighbor]--;
                extendedValues[neighbor] += contribution;

                if (incomingEdges[neighbor] == 1) {
                    processingQueue.push(neighbor);
                }
            }
        }

        return divisibleCount;
    }
};

Follow the code provided to craft a cohesive solution for determining the maximum number of k-divisible components in a graph. The problem specifies a graph given by nodes and edges, and each node holds an integer value. The core task involves processing these nodes and values to find out how many nodes or connected components satisfy the divisibility condition with a given divisor.

Below outlines the essential phases implemented in the code:

  • Initialization Phase:

    • Start by testing a basic condition to handle smaller inputs. If there are fewer than two nodes, immediately return 1 because fewer nodes reduce the complexity of divisibility conditions.
    • Set up initial data structures like adjacency lists and a count of incoming edges for each node. These will be utilized later for navigating through the graph and updating properties of the nodes dynamically.
  • Construction Phase:

    • Construct the adjacency list which represents the graph's structure using provided node connections.
    • Simultaneously, update the count of incoming edges for each node. These counts help identify leaf nodes, which are pivotal for the later breadth-first search (BFS) process.
  • Processing Phase:

    • Employ a BFS algorithm starting from the leaf nodes. This choice simplifies the calculation by collapsing the problem from the boundaries inward.
    • Each leaf node starts with no contribution. Check if the value of the node is divisible by the given divisor. If divisible, increase the count of divisible nodes. If not, this node's value becomes a contribution to its neighbors.
    • Update and propagate node contributions through the graph while ensuring to update the adjacency properties and preventing redundancy and loops. This propagation helps calculate combined values which might turn their division result different from when checked initially.
  • Termination and Result Compilation:

    • Terminate the BFS once all nodes are processed according to their incoming edges, meaning each has contributed its potential to its neighbors.
    • Return the count of nodes that individually satisfy the divisibility condition.

The above method provides a structured approach to tackling the problem by breaking down the graph into manageable parts, starting from outer leaves inward and leveraging properties like divisibility to incrementally build up the solution. This method is efficient in terms of understanding the graph's structure and evolving each node's value in relation to its neighbors.

java
class Solution {

    public int countValidComponents(
        int vertices,
        int[][] connections,
        int[] nodeValues,
        int divisor
    ) {
        if (vertices < 2) return 1; // Early exit for very small graphs

        int validCount = 0;
        List<List<Integer>> adjacencyList = new ArrayList<>();
        int[] degrees = new int[vertices];

        // Construct the graph structure
        for (int i = 0; i < vertices; i++) {
            adjacencyList.add(new ArrayList<>()); // Initialize adjacency list for each vertex
        }
        for (int[] connection : connections) {
            int from = connection[0], to = connection[1];
            adjacencyList.get(from).add(to);
            adjacencyList.get(to).add(from);
            degrees[from]++;
            degrees[to]++;
        }

        // Convert node integers to long to handle large sums
        long[] extendedValues = new long[vertices];
        for (int i = 0; i < vertices; i++) {
            extendedValues[i] = nodeValues[i];
        }

        // Begin with leaf nodes which have exactly one connection
        Queue<Integer> leafQueue = new LinkedList<>();
        for (int vertex = 0; vertex < vertices; vertex++) {
            if (degrees[vertex] == 1) {
                leafQueue.offer(vertex);
            }
        }

        while (!leafQueue.isEmpty()) {
            int currentVertex = leafQueue.poll();
            degrees[currentVertex]--;

            long accumulatedValue = 0;

            // Check divisibility condition
            if (extendedValues[currentVertex] % divisor == 0) {
                validCount++;
            } else {
                accumulatedValue = extendedValues[currentVertex];
            }

            // Update neighboring vertices
            for (int neighbor : adjacencyList.get(currentVertex)) {
                if (degrees[neighbor] == 0) {
                    continue;
                }

                degrees[neighbor]--;
                extendedValues[neighbor] += accumulatedValue;

                // Enqueue the new leaf nodes
                if (degrees[neighbor] == 1) {
                    leafQueue.offer(neighbor);
                }
            }
        }

        return validCount;
    }
}

This Java solution addresses the problem of counting k-divisible components in a graph where each node has a specific integer value. The main goal is to determine how many nodes, or sets of connected nodes, have a combined value that is divisible by a specified divisor k.

  • Initialize an adjacency list to represent the graph, and an array degrees to keep track of the number of connections (or edges) each vertex has.
  • For each vertex, populate the adjacency list and degrees array from the provided connections.
  • Convert all node values from integers to longs for handling large number values, to ensure accuracy during sum operations.
  • Identify all leaf nodes (nodes with only one connection) and start from there using a queue.
  • While processing each node from the queue, decrement its degree and check whether its combined value (with possible accumulated values from previously processed nodes) is divisible by the divisor. If divisible, increase the count of valid components.
  • For each of its neighbors, update their degrees, accumulate their values, and if a neighbor turns into a leaf node (degree becomes one), add it to the queue for processing.
  • Continue this process until all potential leaf nodes are processed. The final count of valid components is then returned.

This approach leverages queuing leaf nodes and processing them to reduce complexity and efficiently handle accumulation of node values, making it apt for large graphs where direct computation could be less feasible.

python
class Solution:
    def countComponentsDivisibleByK(
        self, num_nodes: int, connections: List[List[int]], node_values: List[int], divisor: int
    ) -> int:
        if num_nodes < 2:
            return 1
        total_count = 0
        connectivity = defaultdict(list)
        node_degrees = [0] * num_nodes

        # Construct the graph and tally node degrees
        for start, end in connections:
            connectivity[start].append(end)
            connectivity[end].append(start)
            node_degrees[start] += 1
            node_degrees[end] += 1

        # Use deque to handle nodes with a single connection
        leaf_nodes = deque(i for i in range(num_nodes) if node_degrees[i] == 1)

        while leaf_nodes:
            current = leaf_nodes.popleft()
            node_degrees[current] -= 1
            temp_value = 0

            # Check divisibility by divisor
            if node_values[current] % divisor == 0:
                total_count += 1
            else:
                temp_value = node_values[current]

            # Adjust values for adjacent nodes
            for adjacent in connectivity[current]:
                if node_degrees[adjacent] == 0:
                    continue
                node_degrees[adjacent] -= 1
                node_values[adjacent] += temp_value

                # Enqueue nodes with updated degree of one
                if node_degrees[adjacent] == 1:
                    leaf_nodes.append(adjacent)

        return total_count

The given Python code provides a solution to count the number of components in a graph whose node values are divisible by a specified integer divisor. Here's a succinct summary of the steps involved in the script, reflecting how the solution operates:

  1. Start by initializing the total count of nodes fulfilling the divisibility condition to zero and by creating structures to keep track of the graph connectivity and degrees of each node.

  2. Build the undirected graph using an adjacency list from the provided connections and simultaneously, calculate the degree for each node through the node_degrees list.

  3. Utilize a deque to manage leaf nodes, which are nodes with only one connection. Process these nodes to potentially propagate values to their connected neighbors and update conditions based on divisibility by the divisor.

  4. Loop through each node in the leaf_nodes deque, decrement its degree, and then check if its value is divisible by divisor. If divisible, increase the total_count. For non-divisible cases, propagate the node’s value to its adjacent nodes.

  5. Adjust the values of adjacent nodes by adding the current node’s value if previously found non-divisible, and update connectivity effectively by decrementing the degree of adjacent nodes.

  6. Append to leaf_nodes any nodes that become new leaf nodes (nodes with a single remaining connection) after updating degrees.

  7. Return the total_count as the outcome, representing the number of nodes with values divisible by the specified divisor upon processing all connections.

This approach efficiently uses graph traversal methods coupled with manipulations based on node degrees and values to determine the required count of nodes meeting the divisibility condition.

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