Problem Description

You are given an undirected graph with n nodes, where these nodes are labeled from 0 to n-1. This graph is defined by its edges, where each edge connects two different nodes (described by an array of two integers [a, b]) and each edge has an associated probability of successful traversal (given in an array succProb). The task is to find the path from a given starting node start to a target node end that has the maximum probability of successfully reaching the target. If no such path exists, the result should be 0. The probabilities are multiplicative along the path, and the result should be within 1e-5 of the actual answer to be considered correct.

Intuition

The problem is a variant of the classic shortest path problem in graph theory. However, instead of finding the shortest distance, we're looking to maximize the probability of success along a path from start to end.

To approach this issue, we can think of the graph as a weighted graph where each weight is the probability of successfully traversing an edge. Then, we can use a max-heap priority queue to keep track of the most promising paths (i.e., paths with the highest probability). This involves adapting Dijkstra's shortest path algorithm to maximize probabilities instead of minimizing distances.

The core idea is to use a max-heap to continuously explore the graph, picking the node-edge combination with the highest probability, and updating the best-known probabilities to other nodes as the algorithm proceeds (the array d). By pushing negative probabilities onto the heap, we effectively turn our max-heap into a min-heap while ensuring we always pop the currently best path in terms of probability.

Once we've exhausted the search or found the target node, we can return the probability associated with the target node, which at this point is the highest probability to reach it from the start node.

Learn more about Graph, Shortest Path and Heap (Priority Queue) patterns.

Solution Approach

The implementation provided in the solution can be broken down into several key steps using algorithms and data structures:

1. Graph Representation: The use of a defaultdict to create an adjacency list (g) represents the graph. This allows us to dynamically add vertices (node connections) and associate each with its corresponding success probability.

2. Initial Setup:

   • A priority queue q is used, specifically implemented as a max-heap here with negative probabilities, to store the nodes along with their current best probability to reach those nodes.
   • An array d is initialized to hold the maximum probabilities of reaching each node from the start, with only the start node's probability initially set to 1 (since the probability of starting at the start node is certain).

3. Dijkstra's Algorithm Adaptation:

   • We adapt Dijkstra's algorithm in a loop that runs while the priority queue is not empty. We take the node with the current highest probability (most promising path) by using heappop which retrieves and removes the element from the heap with the smallest value, which is actually our largest probability due to the negation trick.
   • We check if the current probability is better than the known probability in the array d for that node (since it's possible that a better path has already been found).

4. Probability Updates and Queue Management:

   • For each of the node's neighbors (v) and traversal probabilities (t), we calculate the new probability to reach v as the product of t and the max probability to reach the current node (d[u]).
   • If this new probability is an improvement, we update d[v] with it and push the new probability (negated) and the node v onto the priority queue.

5. Termination and Result:

   • The loop continues until the priority queue is emptied (all paths are explored) or we arrive at the end node.
   • Finally, we return d[end]. This is the maximum probability of reaching the end node from the start node that was recorded throughout the process.

By using these strategies, this algorithm effectively searches paths from the start node to the end node, always pursuing the most promising route in terms of success probability. The usage of negative values in the max-heap is a clever trick to maintain a priority queue structure that can be used with Python's built-in heapq module, which is naturally a min-heap.

Example Walkthrough

Let's apply the solution approach to a small example graph to illustrate how it works. Assume we have the following graph:

• The graph has 4 nodes (0, 1, 2, 3).
• The edges with their associated success probabilities are:
  • (0, 1) with a probability of 0.5
  • (0, 2) with a probability of 0.2
  • (1, 3) with a probability of 0.8
  • (2, 3) with a probability of 0.9
• The task is to find the maximum probability path from node 0 to node 3.

Step 1: Graph Representation

We represent our graph as an adjacency list: g = { 0: [(1, 0.5), (2, 0.2)], 1: [(0, 0.5), (3, 0.8)], 2: [(0, 0.2), (3, 0.9)], 3: [(1, 0.8), (2, 0.9)] }

Step 2: Initial Setup

• Initialize the priority queue q and insert the starting node with a probability of 1 (since we're starting at node 0): q = [(-1, 0)].
• Our maximum probabilities array d will be: [1, 0, 0, 0].

Step 3: Dijkstra's Algorithm Adaptation

• We pop from the priority queue q. The first node popped is (0, -1). Since -1 (actually 1 in non-negated form) is the highest probability, we start from there.
• Check and update the probabilities of node 0's neighbors (nodes 1 and 2).

Step 4: Probability Updates and Queue Management

• Update probabilities:

  • For node 1 via node 0: d[1] = 0.5.
  • For node 2 via node 0: d[2] = 0.2.

• The priority queue q is updated accordingly: q = [(-0.5, 1), (-0.2, 2)].

• Continue with the next node in q, which is node 1 with a probability of 0.5.

• Update node 1's neighbor:

  • For node 3 via node 1: d[3] = max(d[3], d[1] * 0.8) = 0.5 * 0.8 = 0.4.

• The priority queue is updated: q = [(-0.2, 2), (-0.4, 3)].

• Continue with the next node in q, which is node 2 with a probability of 0.2.

• Update node 2's neighbor:

  • For node 3 via node 2: d[3] = max(d[3], d[2] * 0.9) = max(0.4, 0.2 * 0.9) = 0.18.
  • Here, the probability via node 2 (0.18) is not better than the existing path via node 1 (0.4), so we do not update d[3].

Step 5: Termination and Result

• We arrive at node 3 with the highest probability found of 0.4.
• Since we have explored all possible paths, the algorithm terminates.

The final probabilities array d is [1, 0.5, 0.2, 0.4]. Therefore, the maximum probability of a successful path from node 0 to node 3 is 0.4.

Solution Implementation

Time and Space Complexity

The given code is solving the problem of finding the maximum probability path from the start node to the end node in a graph represented by n nodes and a list of edges with their associated success probabilities.

Time Complexity

The time complexity of the code can be analyzed based on the following points:

1. Building the graph: The graph is built using a dictionary of adjacency lists. The operation involves iterating over every given edge, which occurs in O(E), where E is the number of edges.

2. Initializing the heap and distance array: One heap operation (heappush) is done initially, which is O(1), and initializing the distance array of size n is O(n).

3. While-loop with heap operations: The loop continues until the heap is empty. In the worst case, every vertex is pushed and popped from the heap only once, resulting in at most O(V) heap operations (V is the number of vertices). Each heappop is O(log(V)), and each heappush is O(log(V)). There could be at most E heappush operations since we push to the heap once for every edge in the worst case.

Combining these operations, we get the worst-case time complexity: O(E + V) + O(V) * O(log(V)) + O(E) * O(log(V)) which simplifies to O((V + E) * log(V)).

Space Complexity

The space complexity can be analyzed based on the following factors:

1. Graph storage: The graph uses an adjacency list representation, requiring O(V + E) space, as each edge shows up twice in the adjacency list.

2. Priority queue (heap): In the worst case, the heap can contain all vertices, which adds up to O(V) space.

3. Distance array: An array of size n is used to store the maximum probability for each node from the start node, which is O(V).

Combining these space requirements, we get the total space complexity: O(V + E) + O(V) + O(V) which simplifies to O(V + E).

Learn more about how to find time and space complexity quickly using problem constraints.