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‎13. Graphs/03. LeetCode Medium.ipynb‎

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"Solution().getAncestors(n, edgeList)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 16. [2285]. Maximum Total Importance of Roads"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 387,
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"metadata": {},
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"outputs": [],
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"source": [
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"import collections\n",
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"\n",
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"class Solution:\n",
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" def maximumImportance(self, n: int, roads: List[List[int]]) -> int:\n",
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" indegree = collections.defaultdict(int)\n",
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" for a,b in roads:\n",
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" indegree[b]+=1\n",
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" indegree[a]+=1\n",
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" return sum(map(lambda x:x[0]*x[1] , zip(sorted(indegree.values(), reverse=True), list(range(n,0,-1)))))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 388,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"20"
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]
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},
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"execution_count": 388,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"n = 5; roads = [[0,1],[1,2],[2,3],[0,2],[1,3],[2,4]]\n",
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"n = 5; roads = [[0,3],[2,4],[1,3]]\n",
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"Solution().maximumImportance(n, roads)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 17. 2368. Reachable Nodes With Restrictions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 415,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Solution:\n",
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" def reachableNodes(self, n: int, edges: List[List[int]], restricted: List[int]) -> int:\n",
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" graph = {i:set() for i in range(n)}\n",
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" d = {i:1 for i in restricted}\n",
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" for a,b in edges:\n",
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" graph[a].add(b)\n",
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" graph[b].add(a)\n",
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" stack = collections.deque()\n",
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" stack.append(0)\n",
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" visited = {0:1}\n",
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" res = 1\n",
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" while stack:\n",
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" node = stack.popleft()\n",
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" for keys in graph[node]:\n",
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" if d.get(keys, None) is None:\n",
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" if visited.get(keys,None) is None:\n",
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" stack.append(keys)\n",
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" res += 1\n",
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" visited[keys] = 1 \n",
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" return res\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Solution:\n",
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" def reachableNodes(self, n: int, edges: List[List[int]], restricted: List[int]) -> int:\n",
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" neighbors = collections.defaultdict(list)\n",
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" for node_a, node_b in edges:\n",
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" neighbors[node_a].append(node_b)\n",
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" neighbors[node_b].append(node_a)\n",
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" seen = [False] * n\n",
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" for node in restricted:\n",
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" seen[node] = True\n",
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" \n",
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" ans = 0\n",
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" queue = collections.queue([0])\n",
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" seen[0] = True\n",
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" while queue:\n",
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" curr_node = queue.popleft()\n",
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" ans += 1\n",
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" for next_node in neighbors[curr_node]:\n",
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" if not seen[next_node]:\n",
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" seen[next_node] = True\n",
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" queue.append(next_node)\n",
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" return ans "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 416,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3"
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]
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},
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"execution_count": 416,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"n = 7; edges = [[0,1],[1,2],[3,1],[4,0],[0,5],[5,6]]; restricted = [4,5]\n",
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"n = 7; edges = [[0,1],[0,2],[0,5],[0,4],[3,2],[6,5]]; restricted = [4,2,1]\n",
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"Solution().reachableNodes(n, edges, restricted)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 18. 1334. Find the City With the Smallest Number of Neighbors at a Threshold Distance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 447,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Solution:\n",
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" def findTheCity(self, n, edges, maxd):\n",
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" dis = [[float('inf')] * n for _ in range(n)]\n",
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" for i, j, w in edges:\n",
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" dis[i][j] = dis[j][i] = w\n",
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" for i in range(n):\n",
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" dis[i][i] = 0\n",
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" for k in range(n):\n",
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" for i in range(n):\n",
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" for j in range(n):\n",
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" dis[i][j] = min(dis[i][j], dis[i][k] + dis[k][j])\n",
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" res = {sum(d <= maxd for d in dis[i]): i for i in range(n)}\n",
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" return res[min(res)] "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 448,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3"
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]
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},
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"execution_count": 448,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"n = 4; edges = [[0,1,3],[1,2,1],[1,3,4],[2,3,1]]; distanceThreshold = 4\n",
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"Solution().findTheCity(n, edges, distanceThreshold)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 19."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 20."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 21."
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]
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},
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"cell_type": "code",
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 22."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 23."
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 24."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 25."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

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