# Posts published in “Graph”

On a social network consisting of m users and some friendships between users, two users can communicate with each other if they know a common language.

You are given an integer n, an array languages, and an array friendships where:

• There are n languages numbered 1 through n,
• languages[i] is the set of languages the i​​​​​​th​​​​ user knows, and
• friendships[i] = [u​​​​​​i​​​, v​​​​​​i] denotes a friendship between the users u​​​​​​​​​​​i​​​​​ and vi.

You can choose one language and teach it to some users so that all friends can communicate with each other. Return the minimum number of users you need to teach.Note that friendships are not transitive, meaning if x is a friend of y and y is a friend of z, this doesn’t guarantee that x is a friend of z.

Example 1:

Input: n = 2, languages = [,,[1,2]], friendships = [[1,2],[1,3],[2,3]]
Output: 1
Explanation: You can either teach user 1 the second language or user 2 the first language.


Example 2:

Input: n = 3, languages = [,[1,3],[1,2],], friendships = [[1,4],[1,2],[3,4],[2,3]]
Output: 2
Explanation: Teach the third language to users 1 and 2, yielding two users to teach.


Constraints:

• 2 <= n <= 500
• languages.length == m
• 1 <= m <= 500
• 1 <= languages[i].length <= n
• 1 <= languages[i][j] <= n
• 1 <= u​​​​​​i < v​​​​​​i <= languages.length
• 1 <= friendships.length <= 500
• All tuples (u​​​​​i, v​​​​​​i) are unique
• languages[i] contains only unique values

## Solution: Brute Force

Enumerate all languages and see which one is the best.

If two friends speak a common language, we can skip counting them.

Time complexity: O(m*(n+|friendship|))
Space complexity: O(m*n)

## C++

You are given an array pairs, where pairs[i] = [xi, yi], and:

• There are no duplicates.
• xi < yi

Let ways be the number of rooted trees that satisfy the following conditions:

• The tree consists of nodes whose values appeared in pairs.
• A pair [xi, yi] exists in pairs if and only if xi is an ancestor of yi or yi is an ancestor of xi.
• Note: the tree does not have to be a binary tree.

Two ways are considered to be different if there is at least one node that has different parents in both ways.

Return:

• 0 if ways == 0
• 1 if ways == 1
• 2 if ways > 1

rooted tree is a tree that has a single root node, and all edges are oriented to be outgoing from the root.

An ancestor of a node is any node on the path from the root to that node (excluding the node itself). The root has no ancestors.

Example 1:

Input: pairs = [[1,2],[2,3]]
Output: 1
Explanation: There is exactly one valid rooted tree, which is shown in the above figure.


Example 2:

Input: pairs = [[1,2],[2,3],[1,3]]
Output: 2
Explanation: There are multiple valid rooted trees. Three of them are shown in the above figures.


Example 3:

Input: pairs = [[1,2],[2,3],[2,4],[1,5]]
Output: 0
Explanation: There are no valid rooted trees.

Constraints:

• 1 <= pairs.length <= 105
• 1 <= xi < yi <= 500
• The elements in pairs are unique.

## Solution: Bitset

Time complexity: O(E*V)
Space complexity: O(V^2)

## Python3

An undirected graph of n nodes is defined by edgeList, where edgeList[i] = [ui, vi, disi] denotes an edge between nodes ui and vi with distance disi. Note that there may be multiple edges between two nodes.

Given an array queries, where queries[j] = [pj, qj, limitj], your task is to determine for each queries[j] whether there is a path between pj and qjsuch that each edge on the path has a distance strictly less than limitj .

Return boolean array answer, where answer.length == queries.length and the jth value of answer is true if there is a path for queries[j] is true, and false otherwise.

Example 1:

Input: n = 3, edgeList = [[0,1,2],[1,2,4],[2,0,8],[1,0,16]], queries = [[0,1,2],[0,2,5]]
Output: [false,true]
Explanation: The above figure shows the given graph. Note that there are two overlapping edges between 0 and 1 with distances 2 and 16.
For the first query, between 0 and 1 there is no path where each distance is less than 2, thus we return false for this query.
For the second query, there is a path (0 -> 1 -> 2) of two edges with distances less than 5, thus we return true for this query.


Example 2:

Input: n = 5, edgeList = [[0,1,10],[1,2,5],[2,3,9],[3,4,13]], queries = [[0,4,14],[1,4,13]]
Output: [true,false]
Exaplanation: The above figure shows the given graph.


Constraints:

• 2 <= n <= 105
• 1 <= edgeList.length, queries.length <= 105
• edgeList[i].length == 3
• queries[j].length == 3
• 0 <= ui, vi, pj, qj <= n - 1
• ui != vi
• pj != qj
• 1 <= disi, limitj <= 109
• There may be multiple edges between two nodes.

## Solution: Union Find

Since queries are offline, we can reorder them to optimize time complexity. Answer queries by their limits in ascending order while union edges by weights up to the limit. In this case, we just need to go through the entire edge list at most once.

Time complexity: O(QlogQ + ElogE)
Space complexity: O(Q + E)

## C++

Given an m x n matrix, return a new matrix answer where answer[row][col] is the rank of matrix[row][col].

The rank is an integer that represents how large an element is compared to other elements. It is calculated using the following rules:

• The rank is an integer starting from 1.
• If two elements p and q are in the same row or column, then:
• If p < q then rank(p) < rank(q)
• If p == q then rank(p) == rank(q)
• If p > q then rank(p) > rank(q)
• The rank should be as small as possible.

It is guaranteed that answer is unique under the given rules.

Example 1:

Input: matrix = [[1,2],[3,4]]
Output: [[1,2],[2,3]]
Explanation:
The rank of matrix is 1 because it is the smallest integer in its row and column.
The rank of matrix is 2 because matrix > matrix and matrix is rank 1.
The rank of matrix is 2 because matrix > matrix and matrix is rank 1.
The rank of matrix is 3 because matrix > matrix, matrix > matrix, and both matrix and matrix are rank 2.


Example 2:

Input: matrix = [[7,7],[7,7]]
Output: [[1,1],[1,1]]


Example 3:

Input: matrix = [[20,-21,14],[-19,4,19],[22,-47,24],[-19,4,19]]
Output: [[4,2,3],[1,3,4],[5,1,6],[1,3,4]]


Example 4:

Input: matrix = [[7,3,6],[1,4,5],[9,8,2]]
Output: [[5,1,4],[1,2,3],[6,3,1]]


Constraints:

• m == matrix.length
• n == matrix[i].length
• 1 <= m, n <= 500
• -109 <= matrix[row][col] <= 109

## Solution: Union Find

Group cells by their values, process groups (cells that have the same value) in ascending order (smaller number has smaller rank).

For cells that are in the same row and same cols union them using union find, they should have the same rank which equals to max(max_rank_x[cols], max_rank_y[rows]) + 1.

Time complexity: O(m*n*(m+n))
Space complexity: O(m*n)

## C++

You are a hiker preparing for an upcoming hike. You are given heights, a 2D array of size rows x columns, where heights[row][col] represents the height of cell (row, col). You are situated in the top-left cell, (0, 0), and you hope to travel to the bottom-right cell, (rows-1, columns-1) (i.e., 0-indexed). You can move updownleft, or right, and you wish to find a route that requires the minimum effort.

A route’s effort is the maximum absolute differencein heights between two consecutive cells of the route.

Return the minimum effort required to travel from the top-left cell to the bottom-right cell.

Example 1:

Input: heights = [[1,2,2],[3,8,2],[5,3,5]]
Output: 2
Explanation: The route of [1,3,5,3,5] has a maximum absolute difference of 2 in consecutive cells.
This is better than the route of [1,2,2,2,5], where the maximum absolute difference is 3.


Example 2:

Input: heights = [[1,2,3],[3,8,4],[5,3,5]]
Output: 1
Explanation: The route of [1,2,3,4,5] has a maximum absolute difference of 1 in consecutive cells, which is better than route [1,3,5,3,5].


Example 3:

Input: heights = [[1,2,1,1,1],[1,2,1,2,1],[1,2,1,2,1],[1,2,1,2,1],[1,1,1,2,1]]
Output: 0
Explanation: This route does not require any effort.


Constraints:

• rows == heights.length
• columns == heights[i].length
• 1 <= rows, columns <= 100
• 1 <= heights[i][j] <= 106

## Solution: “Lazy BFS / DP”

dp[y][x] = min(max(dp[ty][tx], abs(h[ty][tx] – h[y][x]))) (x, y) and (tx, ty) are neighbors
repeat this process for at most rows * cols times.
if dp does not change after one round which means we found the optimal solution and can break earlier.

Time complexity: O(n^2*m^2))
Space complexity: O(nm)

## Solution 2: Binary Search + BFS

Use binary search to guess a cost and then check whether there is path that is under the cost.

Time complexity: O(mn*log(max(h) – min(h)))
Space complexity: O(mn)

## Solution 3: Dijkstra

Time complexity: O(mnlog(mn))
Space complexity: O(mn)