You are given an undirected weighted graph of n nodes (0-indexed), represented by an edge list where edges[i] = [a, b] is an undirected edge connecting the nodes a and b with a probability of success of traversing that edge succProb[i].
Given two nodes start and end, find the path with the maximum probability of success to go from start to end and return its success probability.
If there is no path from start to end, return 0. Your answer will be accepted if it differs from the correct answer by at most 1e-5.
 
Example 1:

Input: n = 3, edges = [[0,1],[1,2],[0,2]], succProb = [0.5,0.5,0.2], start = 0, end = 2
Output: 0.25000
Explanation: There are two paths from start to end, one having a probability of success = 0.2 and the other has 0.5 * 0.5 = 0.25.

Example 2:

Input: n = 3, edges = [[0,1],[1,2],[0,2]], succProb = [0.5,0.5,0.3], start = 0, end = 2
Output: 0.30000

Example 3:

Input: n = 3, edges = [[0,1]], succProb = [0.5], start = 0, end = 2
Output: 0.00000
Explanation: There is no path between 0 and 2.

 
Constraints:

2 <= n <= 10^4
0 <= start, end < n
start != end
0 <= a, b < n
a != b
0 <= succProb.length == edges.length <= 2*10^4
0 <= succProb[i] <= 1
There is at most one edge between every two nodes.

Difficulty: MEDIUM
Tags: Graph algorithms

Data structures

Shortest paths
Skill types: Data structures

Starter code:
class Solution:
    def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
        

Solutions:
class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], probs: List[float], s: int, t: int) -> float:
		graph = {u: {} for u in range(n)}
		for ((u, v), prob) in zip(edges, probs):
			graph[u][v] = prob
			graph[v][u] = prob
		frontier = [(-1, s)]
		seen = set()
		while len(frontier) != 0:
			(neg_path_prob, u) = heapq.heappop(frontier)
			if u == t:
				return -neg_path_prob
			seen.add(u)
			for (v, edge_prob) in graph[u].items():
				if v not in seen:
					heapq.heappush(frontier, (neg_path_prob * edge_prob, v))
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = collections.defaultdict(dict)
		for (edge, prob) in zip(edges, succProb):
			(s, t) = edge
			graph[s][t] = prob
			graph[t][s] = prob
		heap = [(-1, start)]
		prob = [0] * n
		prob[start] = 1
		visited = [False] * n
		while heap:
			(curr_prob, node) = heapq.heappop(heap)
			if visited[node]:
				continue
			if node == end:
				return prob[end]
			curr_prob *= -1
			visited[node] = True
			for neighbor in graph[node]:
				if visited[neighbor]:
					continue
				p = curr_prob * graph[node][neighbor]
				if p > prob[neighbor]:
					prob[neighbor] = p
					heapq.heappush(heap, (-p, neighbor))
		return prob[end]


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		weights = list(map(lambda x: -math.log2(x), succProb))
		g = defaultdict(list)
		for ((u, v), w) in zip(edges, weights):
			g[u].append([v, w])
			g[v].append([u, w])
		dist = {}
		q = [(0, start)]
		while q:
			(w, u) = heapq.heappop(q)
			if u in dist:
				continue
			dist[u] = w
			if u == end:
				break
			for (v, w) in g[u]:
				if v not in dist or dist[v] > dist[u] + w:
					heapq.heappush(q, (dist[u] + w, v))
		return 2 ** (-dist[end]) if end in dist else 0.0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = [[] for _ in range(n)]
		for (edge, p) in zip(edges, succProb):
			(a, b) = edge
			graph[a].append((b, p))
			graph[b].append((a, p))
		prob = {start: 1}
		queue = [(start, 1)]
		while queue:
			new_queue = []
			for (a, p_a) in queue:
				for (b, p_b) in graph[a]:
					p = p_a * p_b
					if b not in prob or p > prob[b]:
						prob[b] = p
						new_queue.append((b, p))
			queue = new_queue
		return prob.get(end, 0)


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = defaultdict(list)
		for ((a, b), probability) in zip(edges, succProb):
			graph[a].append([b, probability])
			graph[b].append([a, probability])
		if start == end:
			return 1
		queue = [[-1, start]]
		visited = set()
		distance_map = defaultdict(lambda : float('inf'))
		while queue:
			(prob, node) = heappop(queue)
			visited.add(node)
			if node == end:
				return -prob
			for (neighbour, next_prob) in graph[node]:
				if neighbour in visited:
					continue
				heappush(queue, [prob * next_prob, neighbour])
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		dp = [0.0 for _ in range(start)] + [1.0] + [0.0 for _ in range(start + 1, n)]
		dic = defaultdict(list)
		for (e, p) in zip(edges, succProb):
			dic[e[0]].append((e[1], p))
			dic[e[1]].append((e[0], p))
		queue = deque([start])
		while queue:
			for _ in range(len(queue)):
				cur = queue.popleft()
				for (c, p) in dic[cur]:
					if dp[cur] * p > dp[c]:
						dp[c] = dp[cur] * p
						queue.append(c)
		return dp[end]


from heapq import heappop, heappush
from sys import maxsize as inf
from collections import defaultdict as dic

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = dic(dict)
		for ([u, v], w) in zip(edges, succProb):
			graph[u][v] = w
			graph[v][u] = w
		dist = {i: 0 for i in range(n)}
		dist[start] = 1
		visited = set()
		max_prob = [(-1 * 1, start)]
		while len(max_prob):
			(cur_prob, cur_node) = heappop(max_prob)
			if cur_node in visited:
				continue
			visited.add(cur_node)
			for nei in graph[cur_node]:
				if nei in visited:
					continue
				if dist[nei] < graph[cur_node][nei] * (-1 * cur_prob):
					dist[nei] = graph[cur_node][nei] * (-1 * cur_prob)
					heappush(max_prob, (-1 * dist[nei], nei))
			if cur_node == end:
				break
		return dist[end]


from collections import defaultdict as ddict
from collections import deque

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		maxProb = ddict(float)
		graph = ddict(dict)
		for ([a, b], sucprob) in zip(edges, succProb):
			graph[a][b] = sucprob if b not in graph[a] else max(sucprob, graph[a][b])
			graph[b][a] = sucprob if a not in graph[b] else max(sucprob, graph[b][a])
		print(graph)
		que = deque()
		que.append((start, 1))
		while que:
			(node, prob) = que.popleft()
			maxProb[node] = max(maxProb[node], prob)
			for child in graph[node]:
				if prob * graph[node][child] > maxProb[child]:
					que.append((child, prob * graph[node][child]))
		return maxProb[end]


from heapq import heappush, heappop

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		stack = [(-1, start)]
		paths = [0 for i in range(n)]
		paths[start] = 1
		graph = [{} for i in range(n)]
		for ((source, dstn), weight) in zip(edges, succProb):
			graph[source][dstn] = graph[dstn][source] = weight
		while stack:
			(_, current) = heappop(stack)
			for v in graph[current]:
				temp = paths[current] * graph[current][v]
				if temp > paths[v]:
					heappush(stack, (-temp, v))
					paths[v] = max(temp, paths[v])
		return paths[end]


import heapq

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		adj = {i: [] for i in range(n)}
		best = [0] * n
		for (edge, prob) in zip(edges, succProb):
			(u, v) = edge
			adj[u].append((v, prob))
			adj[v].append((u, prob))
		queue = []
		heapq.heappush(queue, (-1, start))
		while queue:
			(prob, v) = heapq.heappop(queue)
			prob = -prob
			if v == end:
				return prob
			if prob < best[v]:
				continue
			best[v] = prob
			for (nxt, np) in adj[v]:
				if nxt != v:
					tmp = np * prob
					if tmp > best[nxt]:
						best[nxt] = tmp
						heapq.heappush(queue, (-tmp, nxt))
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		dp = [0.0 for _ in range(start)] + [1.0] + [0.0 for _ in range(start + 1, n)]
		graph = defaultdict(list)
		for ((u, v), p) in zip(edges, succProb):
			graph[u].append((v, p))
			graph[v].append((u, p))
		selected = set()
		heap = [(-1.0, start)]
		while heap:
			(p, cur) = heapq.heappop(heap)
			selected.add(cur)
			if cur == end:
				return -p
			for (child, prob) in graph[cur]:
				if child not in selected and dp[cur] * prob > dp[child]:
					dp[child] = dp[cur] * prob
					heapq.heappush(heap, (-dp[child], child))
		return 0


from collections import defaultdict, deque

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		stack = deque([start])
		paths = [0 for i in range(n)]
		paths[start] = 1
		graph = [{} for i in range(n)]
		for ((source, dstn), weight) in zip(edges, succProb):
			graph[source][dstn] = graph[dstn][source] = weight
		while stack:
			current = stack.popleft()
			for v in graph[current]:
				temp = paths[current] * graph[current][v]
				if temp > paths[v]:
					stack.append(v)
					paths[v] = max(temp, paths[v])
		return paths[end]


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:

		def method1():
			graph = collections.defaultdict(set)
			for (i, pair) in enumerate(edges):
				(u, v) = pair
				graph[u].add((v, succProb[i]))
				graph[v].add((u, succProb[i]))
			seen = {0: 0}
			queue = [(start, -1)]
			ans = float('-inf')
			while queue:
				(node, p) = heapq.heappop(queue)
				p = -p
				if node == end:
					ans = max(ans, p)
				for (nei, dw) in graph[node]:
					if (nei not in seen or seen[nei] < p * dw) and p * dw > ans:
						seen[nei] = p * dw
						heapq.heappush(queue, (nei, -p * dw))
			return ans if ans != float('-inf') else 0

		def method2():
			AdjList = [set() for _ in range(n)]
			for ((u, v), p) in zip(edges, succProb):
				AdjList[u].add((v, log2(1 / p)))
				AdjList[v].add((u, log2(1 / p)))
			dist = [float('inf') for _ in range(n)]
			dist[start] = 0
			h = [(0, start)]
			while h:
				(d, u) = heappop(h)
				if d == dist[u]:
					for (v, p) in AdjList[u]:
						if dist[u] + p < dist[v]:
							dist[v] = dist[u] + p
							heappush(h, (dist[v], v))
			return 1 / 2 ** dist[end]
		return method2()


from collections import defaultdict, deque
import bisect
from sortedcontainers import SortedList

class Solution:

	def maxProbability(self, n: int, edges, succProb, start: int, end: int) -> float:
		m = [{} for i in range(n)]
		for (edge, d) in zip(edges, succProb):
			(s, e) = edge
			m[s][e] = m[e][s] = d
		queue = deque([start])
		seen = [0] * n
		seen[start] = 1
		while queue:
			s = queue.popleft()
			prob = seen[s]
			for (e, p) in m[s].items():
				tmp = prob * p
				if seen[e] < tmp:
					seen[e] = tmp
					queue.append(e)
		return seen[end]


from heapq import heappush, heappop
from math import log2

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		AdjList = [set() for _ in range(n)]
		for ((u, v), p) in zip(edges, succProb):
			AdjList[u].add((v, log2(1 / p)))
			AdjList[v].add((u, log2(1 / p)))
		dist = [float('inf') for _ in range(n)]
		dist[start] = 0
		h = [(0, start)]
		while h:
			(d, u) = heappop(h)
			if True:
				for (v, p) in AdjList[u]:
					if dist[u] + p < dist[v]:
						dist[v] = dist[u] + p
						heappush(h, (dist[v], v))
		return 1 / 2 ** dist[end]


class Solution:

	def maxProbability(self, n, edges, succProb, start, end) -> float:
		g = [[] for _ in range(n)]
		prob = [0.0 for _ in range(n)]
		prob[start] = 1.0
		for ((a, b), p) in zip(edges, succProb):
			g[a].append((p, b))
			g[b].append((p, a))
		pq = [(-1.0, start)]
		while pq:
			(p, v) = heapq.heappop(pq)
			if prob[v] + p:
				continue
			if v == end:
				return prob[v]
			for (ep, n) in g[v]:
				_p = ep * prob[v]
				if _p > prob[n]:
					prob[n] = _p
					heapq.heappush(pq, (-_p, n))
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], probs: List[float], s: int, t: int) -> float:
		graph = {u: {} for u in range(n)}
		for ((u, v), prob) in zip(edges, probs):
			graph[u][v] = prob
			graph[v][u] = prob
		frontier = [s]
		path_probs = {u: 0 for u in range(n)}
		path_probs[s] = 1
		while len(frontier) != 0:
			u = frontier.pop(0)
			path_prob = path_probs[u]
			for (v, edge_prob) in graph[u].items():
				if path_prob * edge_prob > path_probs[v]:
					path_probs[v] = path_prob * edge_prob
					frontier.append(v)
		return path_probs[t]


import heapq
from math import log2

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		AdjList = [set() for _ in range(n)]
		for ((u, v), prob) in zip(edges, succProb):
			AdjList[u].add((-log2(prob), v))
			AdjList[v].add((-log2(prob), u))
		dist = [float('inf')] * n
		dist[start] = 0
		heap = [(0, start)]
		explored = set()
		while heap:
			(d, curr) = heapq.heappop(heap)
			explored.add(curr)
			for (val, node) in AdjList[curr]:
				if node not in explored:
					if dist[curr] + val < dist[node]:
						dist[node] = dist[curr] + val
						heapq.heappush(heap, (dist[node], node))
		return 2 ** (-dist[end])


from collections import deque

class Solution:

	def maxProbability(self, n: int, edges, succProb, start: int, end: int) -> float:
		m = [{} for i in range(n)]
		for (edge, d) in zip(edges, succProb):
			(s, e) = edge
			m[s][e] = m[e][s] = d
		res = [0] * n
		res[start] = 1
		q = [[-1, start]]
		while q and res[end] == 0:
			tmp = heapq.heappop(q)
			(prob, cur) = (-tmp[0], tmp[1])
			res[cur] = prob
			for (nxt, np) in m[cur].items():
				if res[nxt] != 0:
					continue
				heapq.heappush(q, [-np * prob, nxt])
		return res[end]


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = collections.defaultdict(list)
		for ((s, e), prob) in zip(edges, succProb):
			graph[s].append((e, prob))
			graph[e].append((s, prob))
		visited = [0] * n
		prio = [(-1, start)]
		while prio:
			(prob, vertex) = heapq.heappop(prio)
			if prob > visited[vertex]:
				continue
			visited[vertex] = prob
			if vertex == end:
				return -prob
			for (neighbor, cost) in graph[vertex]:
				if prob * cost < visited[neighbor]:
					visited[neighbor] = prob * cost
					heapq.heappush(prio, (prob * cost, neighbor))
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		g = defaultdict(list)
		for (edge, prob) in zip(edges, succProb):
			(x, y) = edge
			g[x].append((prob, y))
			g[y].append((prob, x))
		print(g)
		(q, vis) = ([(-1, start)], set())
		cand = []
		while q:
			(prob, node) = heapq.heappop(q)
			if node == end:
				cand.append(-prob)
			if node not in vis:
				vis.add(node)
				for (next_prob, nbrs) in g[node]:
					if nbrs not in vis:
						heapq.heappush(q, (prob * next_prob, nbrs))
		print(cand)
		return max(cand, default=0)


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		N = 100010
		dist = [2 for i in range(N)]
		seen = set()
		h = [-1 for i in range(N)]
		e = {}
		ne = {}
		w = {}
		idx = 0

		def add(a, b, c, idx):
			(e[idx], w[idx]) = (b, c)
			ne[idx] = h[a]
			h[a] = idx
		for (edge, prob) in zip(edges, succProb):
			add(edge[0], edge[1], prob, idx)
			idx += 1
			add(edge[1], edge[0], prob, idx)
			idx += 1

		def heap_dijkstra():
			dist[start] = 1
			heap = []
			heapq.heappush(heap, (-1, start))
			while heap:
				t = heapq.heappop(heap)
				(vertex, distance) = (t[1], -t[0])
				if vertex in seen:
					continue
				seen.add(vertex)
				i = h[vertex]
				while i != -1:
					j = e[i]
					if dist[j] == 2 or dist[j] < distance * w[i]:
						dist[j] = distance * w[i]
						heapq.heappush(heap, (-dist[j], j))
					i = ne[i]
			return dist[end]
		output = heap_dijkstra()
		if output == 2 or output < 1e-05:
			return 0.0
		else:
			return output


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		dist = [-math.inf for _ in range(n)]
		dist[start] = 1
		max_heap = [(1, start)]
		graph = collections.defaultdict(list)
		for ((u, v), w) in zip(edges, succProb):
			graph[u].append((v, w))
			graph[v].append((u, w))
		while max_heap:
			(prob, node) = heapq.heappop(max_heap)
			prob = abs(prob)
			if node == end:
				return prob
			for (adj, next_prob) in graph[node]:
				next_prob *= prob
				if dist[adj] < next_prob:
					dist[adj] = next_prob
					heapq.heappush(max_heap, (-next_prob, adj))
		return 0.0


from collections import deque
from heapq import heappush, heappop

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = [[] for _ in range(n)]
		for ((u, v), wt) in zip(edges, succProb):
			graph[u].append((v, wt))
			graph[v].append((u, wt))
		probabilities = [0 for _ in range(n)]
		probabilities[start] = 1
		h = [(-probabilities[start], start)]
		visited = set()
		while h:
			(curr_prob, curr_node) = heappop(h)
			curr_prob *= -1
			visited.add(curr_node)
			for (v, wt) in graph[curr_node]:
				if v not in visited:
					if probabilities[curr_node] * wt > probabilities[v]:
						probabilities[v] = probabilities[curr_node] * wt
						heappush(h, (-probabilities[v], v))
		return probabilities[end]


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		dist = [0.0] * n
		dist[start] = 1.0
		for i in range(n):
			flag = False
			for ((a, b), p) in zip(edges, succProb):
				if dist[a] == 0.0 and dist[b] == 0.0:
					continue
				Max = max(dist[a], dist[b]) * p
				if Max > dist[a]:
					dist[a] = Max
					flag = True
				if Max > dist[b]:
					dist[b] = Max
					flag = True
			if not flag:
				break
		return dist[end]


from collections import defaultdict
from heapq import heappush, heappop
from typing import List

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		adjlist = defaultdict(list)
		for (edge, succ) in zip(edges, succProb):
			adjlist[edge[0]].append([edge[1], succ])
			adjlist[edge[1]].append([edge[0], succ])
		cost = [float('-inf')] * n
		hp = [(-1, start)]
		visited = set()
		while hp:
			(price, node) = heappop(hp)
			price = -price
			if node in visited:
				continue
			for (neigh, neighcost) in adjlist[node]:
				heappush(hp, (-(price * neighcost), neigh))
			cost[node] = price
			visited.add(node)
		print(cost)
		if cost[end] == float('-inf'):
			return 0
		return cost[end]


from heapq import *
from collections import defaultdict

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		graph = defaultdict(list)
		for (edge, prob) in zip(edges, succProb):
			(src, dst) = edge
			graph[src].append((dst, prob))
			graph[dst].append((src, prob))
		max_heap = [(-1.0, start, None)]
		while max_heap:
			(probability, node, parent) = heappop(max_heap)
			probability = -probability
			if node == end:
				return probability
			neighbors = graph[node]
			for (neighbor, weight) in neighbors:
				if neighbor != parent:
					heappush(max_heap, (-weight * probability, neighbor, node))
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		g = collections.defaultdict(list)
		for ((u, v), p) in zip(edges, succProb):
			g[u].append((v, p))
			g[v].append((u, p))
		q = [(-1, start)]
		seen = set()
		while q:
			(p, u) = heapq.heappop(q)
			if u in seen:
				continue
			seen.add(u)
			if u == end:
				return -p
			for (v, pp) in g[u]:
				if v not in seen:
					heapq.heappush(q, (p * pp, v))
		return 0


from collections import defaultdict
from heapq import heappop, heappush

class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		adjlist = defaultdict(list)
		for (pair, prob) in zip(edges, succProb):
			(p, c) = pair
			adjlist[p].append([c, prob])
			adjlist[c].append([p, prob])
		probability = {i: float('-inf') for i in range(n)}
		hq = [[-1, start]]
		visited = set()
		while hq:
			(cost, curr) = heappop(hq)
			cost = -cost
			if curr in visited:
				continue
			for (child, price) in adjlist[curr]:
				heappush(hq, [-(price * cost), child])
			visited.add(curr)
			if probability[curr] < cost:
				probability[curr] = cost
		if probability[end] == float('-inf'):
			return 0
		return probability[end]


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		G = defaultdict(list)
		P = {}
		for ((i, j), p) in zip(edges, succProb):
			G[i].append(j)
			G[j].append(i)
			P[i, j] = P[j, i] = p
		hp = [(-1, start)]
		seen = {start: 1}
		while hp:
			(prob, node) = heapq.heappop(hp)
			prob *= -1
			if node == end:
				return prob
			for nei in G[node]:
				tmp = seen[node] * P[node, nei]
				if nei not in seen or seen[nei] < tmp:
					heapq.heappush(hp, (-tmp, nei))
					seen[nei] = tmp
		return 0


class Solution:

	def maxProbability(self, n: int, edges: List[List[int]], succProb: List[float], start: int, end: int) -> float:
		dp = [0.0 for _ in range(start)] + [1.0] + [0.0 for _ in range(n)]
		graph = defaultdict(list)
		for ((u, v), p) in zip(edges, succProb):
			graph[u].append((v, p))
			graph[v].append((u, p))
		queue = [(-1.0, start)]
		while queue:
			for _ in range(len(queue)):
				(p, cur) = heapq.heappop(queue)
				if cur == end:
					return -p
				for (child, prob) in graph[cur]:
					if dp[cur] * prob > dp[child]:
						dp[child] = dp[cur] * prob
						heapq.heappush(queue, (-dp[child], child))
		return 0