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3123. Find Edges in Shortest Paths

Description

You are given an undirected weighted graph of n nodes numbered from 0 to n - 1. The graph consists of m edges represented by a 2D array edges, where edges[i] = [ai, bi, wi] indicates that there is an edge between nodes ai and bi with weight wi.

Consider all the shortest paths from node 0 to node n - 1 in the graph. You need to find a boolean array answer where answer[i] is true if the edge edges[i] is part of at least one shortest path. Otherwise, answer[i] is false.

Return the array answer.

Note that the graph may not be connected.

 

Example 1:

Input: n = 6, edges = [[0,1,4],[0,2,1],[1,3,2],[1,4,3],[1,5,1],[2,3,1],[3,5,3],[4,5,2]]

Output: [true,true,true,false,true,true,true,false]

Explanation:

The following are all the shortest paths between nodes 0 and 5:

  • The path 0 -> 1 -> 5: The sum of weights is 4 + 1 = 5.
  • The path 0 -> 2 -> 3 -> 5: The sum of weights is 1 + 1 + 3 = 5.
  • The path 0 -> 2 -> 3 -> 1 -> 5: The sum of weights is 1 + 1 + 2 + 1 = 5.

Example 2:

Input: n = 4, edges = [[2,0,1],[0,1,1],[0,3,4],[3,2,2]]

Output: [true,false,false,true]

Explanation:

There is one shortest path between nodes 0 and 3, which is the path 0 -> 2 -> 3 with the sum of weights 1 + 2 = 3.

 

Constraints:

  • 2 <= n <= 5 * 104
  • m == edges.length
  • 1 <= m <= min(5 * 104, n * (n - 1) / 2)
  • 0 <= ai, bi < n
  • ai != bi
  • 1 <= wi <= 105
  • There are no repeated edges.

Solutions

Solution 1: Heap Optimized Dijkstra

First, we create an adjacency list $g$ to store the edges of the graph. Then we create an array $dist$ to store the shortest distance from node $0$ to other nodes. We initialize $dist[0] = 0$, and the distance of other nodes is initialized to infinity.

Then, we use the Dijkstra algorithm to calculate the shortest distance from node $0$ to other nodes. The specific steps are as follows:

  1. Create a priority queue $q$ to store the distance and node number of the nodes. Initially, add node $0$ to the queue with a distance of $0$.
  2. Take a node $a$ from the queue. If the distance $da$ of $a$ is greater than $dist[a]$, it means that $a$ has been updated, so skip it directly.
  3. Traverse all neighbor nodes $b$ of node $a$. If $dist[b] > dist[a] + w$, update $dist[b] = dist[a] + w$, and add node $b$ to the queue.
  4. Repeat steps 2 and 3 until the queue is empty.

Next, we create an answer array $ans$ of length $m$, initially all elements are $false$. If $dist[n - 1]$ is infinity, it means that node $0$ cannot reach node $n - 1$, return $ans$ directly. Otherwise, we start from node $n - 1$, traverse all edges, if the edge $(a, b, i)$ satisfies $dist[a] = dist[b] + w$, set $ans[i]$ to $true$, and add node $b$ to the queue.

Finally, return the answer.

The time complexity is $O(m \times \log m)$, and the space complexity is $O(n + m)$, where $n$ and $m$ are the number of nodes and edges respectively.

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class Solution:
    def findAnswer(self, n: int, edges: List[List[int]]) -> List[bool]:
        g = defaultdict(list)
        for i, (a, b, w) in enumerate(edges):
            g[a].append((b, w, i))
            g[b].append((a, w, i))
        dist = [inf] * n
        dist[0] = 0
        q = [(0, 0)]
        while q:
            da, a = heappop(q)
            if da > dist[a]:
                continue
            for b, w, _ in g[a]:
                if dist[b] > dist[a] + w:
                    dist[b] = dist[a] + w
                    heappush(q, (dist[b], b))
        m = len(edges)
        ans = [False] * m
        if dist[n - 1] == inf:
            return ans
        q = deque([n - 1])
        while q:
            a = q.popleft()
            for b, w, i in g[a]:
                if dist[a] == dist[b] + w:
                    ans[i] = True
                    q.append(b)
        return ans
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class Solution {
    public boolean[] findAnswer(int n, int[][] edges) {
        List<int[]>[] g = new List[n];
        Arrays.setAll(g, k -> new ArrayList<>());
        int m = edges.length;
        for (int i = 0; i < m; ++i) {
            int a = edges[i][0], b = edges[i][1], w = edges[i][2];
            g[a].add(new int[] {b, w, i});
            g[b].add(new int[] {a, w, i});
        }
        int[] dist = new int[n];
        final int inf = 1 << 30;
        Arrays.fill(dist, inf);
        dist[0] = 0;
        PriorityQueue<int[]> pq = new PriorityQueue<>((a, b) -> a[0] - b[0]);
        pq.offer(new int[] {0, 0});
        while (!pq.isEmpty()) {
            var p = pq.poll();
            int da = p[0], a = p[1];
            if (da > dist[a]) {
                continue;
            }
            for (var e : g[a]) {
                int b = e[0], w = e[1];
                if (dist[b] > dist[a] + w) {
                    dist[b] = dist[