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        "# Lecture 19 - Breadth First Search : Applications\n",
        "\n",
        "In today's lecture we will discuss -\n",
        "\n",
        "1.   BFS - Design and analysis (review)\n",
        "2.   Queues - FIFO, Enquque, Dequeue (review)\n",
        "2.   BFS application : s-t connectivity\n",
        "3.   BFS application : Shortest paths\n",
        "4.   BFS application : Finding 2nd degree connections\n",
        "5.   Limitations of BFS - cheapest flight route\n",
        "\n",
        "#Learning Objectives -\n",
        "\n",
        "1. BFS - exploration strategy\n",
        "2. Queue operations and time complexities\n",
        "3. Designing algorithms using BFS to solve graph problems - connectivity, shortest paths, second degree connections\n",
        "4. Limitations of BFS\n",
        "\n",
        "**Announcements**\n",
        "\n",
        "1. Homework 4 due Next monday\n",
        "2. Exam 2 slot reservations\n",
        "3. Office hours today (5pm-9pm)\n",
        "4. Exam 2 review session recording (course website)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "# Discovery clicker\n",
        "\n",
        "Scan and use join code 277\n",
        "\n",
        "OR Visit clicker.cs.illinois.edu and use join code 277\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "LktxlwG4nuPs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Depth First Search - Shortcomings\n",
        "\n",
        "* DFS does not guarantee finding the shortest path between given vertices $s$ and $t$. This is often an important consideration. (Consider finding a flight route from Champaign to Melbourne with the fewest number of hops)\n",
        "\n",
        "* This is primarily because DFS isn't set up to find the shortest path. It is designed instead to explore the graph in a depth first manner.\n",
        "\n",
        "In the graph shown below, DFS starting at vertex 0 will identify path 0-1-2 as the path from 0 to 2 while the shortest path is 0-2.\n",
        "\n",
        "![Screenshot 2026-10-23 at 12.53.16 PM.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "0omFcXREmJbu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Breadth First Search\n",
        "\n",
        "Breadth first Search explores the graph in a breadth-first manner. The main idea here is to explore all edges of the start vertex before looking at all edges going out of each of its neighbors.\n",
        "\n",
        "BFS starting out from the start vertex expands the frontier one level/layer at a time, if you will.\n",
        "\n",
        "Let us look at the working of BFS on undirected graphs.\n",
        "\n",
        "https://visualgo.net/en/dfsbfs"
      ],
      "metadata": {
        "id": "GghZc9xlrpUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Breadth based exploration strategy for graphs\n",
        "\n",
        "#Main idea - Start from a vertex and visit all of its neighbors first. Then, repeat this process starting from the neighbors.\n",
        "\n",
        "#Requirements -\n",
        "# 1. Need to keep track of visited vertices (in order to avoid repeated processing of vertices)\n",
        "# 2. Need to store all neighbors somewhere so that we can then visit all of their neighbors in the next level\n",
        "# Since we need to process vertices in the order in which they were visited,\n",
        "#  we need a data structure that helps access vertices in the order in which they were visited.\n",
        "\n",
        "# BFS helps find shortest path lengths - and we can use a distance list to maintain the distance of each vertex from the start vertex\n",
        "\n",
        "#BFS uses the Queue data strcuture to support this requirement.\n"
      ],
      "metadata": {
        "id": "F0TpQDyy526R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import deque\n",
        "def bfs(G,s):\n",
        "  # number of vertices in G\n",
        "  n = len(G)\n",
        "\n",
        "  # initialize dist to be max possible distance\n",
        "  dist = [float('inf')]*len(G)\n",
        "\n",
        "  # noting down the parent of each vertex\n",
        "  parent = [None]*len(G)\n",
        "\n",
        "  # start the search from s\n",
        "  dist[s] = 0\n",
        "  queue = deque()\n",
        "  queue.append(s)\n",
        "\n",
        "  # start exploring\n",
        "  while len(queue) != 0:\n",
        "    # remove first element in the queue and explore its neighbors\n",
        "    u = queue.popleft()    #pops leftmost element in queue (earliest added element)\n",
        "    for v in G[u]:\n",
        "      if dist[v] == float('inf'):\n",
        "        # v has not been visited\n",
        "        dist[v] = dist[u] + 1\n",
        "        parent[v] = u\n",
        "        queue.append(v)\n",
        "\n",
        "  # return the list distance storing the shortest distance to each vertex\n",
        "  return dist,parent\n",
        "\n",
        "\n",
        "print(bfs([[1,2],[0,2],[0,1,3],[2]],0))\n",
        "print(bfs([[1,2],[0,3,4,5],[0],[1,5,6],[1,6,7],[1,3],[3,4],[4]],0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlxkWnWorp4Z",
        "outputId": "4110c848-03ab-490a-c3e1-d548994ca4b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "([0, 1, 1, 2], [None, 0, 0, 2])\n",
            "([0, 1, 1, 2, 2, 2, 3, 3], [None, 0, 0, 1, 1, 1, 3, 4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BFS simulation\n",
        "\n",
        "On the following graph, assuming BFS starts at vertex 0 -\n",
        "\n",
        "![Screenshot 2026-10-23 at 12.53.16 PM.png](data:image/png;base64,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)\n",
        "\n",
        "** Start **\n",
        "\n",
        "* The queue is populated with 0 - queue : [0]\n",
        "* The distance of 0 is set to 0 since it is the start vertex.\n",
        "\n",
        "** Processing vertex 0**\n",
        "\n",
        "* Dequeue operation results in 0 being removed from the queue for processing.\n",
        "queue = []\n",
        "\n",
        "* All of 0's edges will then be processed -\n",
        "\n",
        "* Edge (0,1) - Since 1 is not yet visited, it will be added to the queue. queue : [1]\n",
        "\n",
        "The distance of 1 is set to 1 since the path 0-1 has length 1 and 1's parent is set to 0.\n",
        "\n",
        "* Edge (0,2) - Since 2 is not yet visited, it will be added to the queue.\n",
        "queue : [1,2]\n",
        "\n",
        "The distance of 2 is set to 1 since the path 0-2 has length 1 and 2's parent is set to 0.\n",
        "\n",
        "** Processing vertex 1**\n",
        "\n",
        "* Dequeue operation results in 1 being removed from the queue for processing.\n",
        "queue : [2]\n",
        "\n",
        "* All of 1's edges will then be processed -\n",
        "\n",
        "* Edge (1,0) - Since 0 has already been visited, move on.\n",
        "queue : [2]\n",
        "\n",
        "* Edge (1,2) - Since 2 has already been visited, move on.\n",
        "queue : [2]\n",
        "\n",
        "** Processing vertex 2**\n",
        "\n",
        "* Dequeue operation results in 2 being removed from the queue for processing.\n",
        "queue : []\n",
        "\n",
        "* All of 2's edges will then be processed -\n",
        "\n",
        "* Edge (2,0) - Since 0 has already been visited, move on.\n",
        "queue : []\n",
        "\n",
        "* Edge (2,1) - Since 1 has already been visited, move on.\n",
        "queue : []\n",
        "\n",
        "* Edge (2,3) - Since 3 is not yet visited, it will be added to the queue.\n",
        "queue : [3]\n",
        "\n",
        "The distance of 3 is set to 2 since the path 0-2-3 has length 2 and 3's parent is set to 2.\n",
        "\n",
        "** Processing vertex 3**\n",
        "\n",
        "* Dequeue operation results in 3 being removed from the queue for processing.\n",
        "queue : []\n",
        "\n",
        "* All of 3's edges will then be processed -\n",
        "\n",
        "* Edge (3,2) - Since 2 has already been visited, move on.\n",
        "queue : []\n",
        "\n",
        "** END **\n",
        "\n",
        "The queue is now empty, and all vertices reachable from 0 have been assigned distance and parent values.\n"
      ],
      "metadata": {
        "id": "VKV4fkDoP1uI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BFS : Analysis\n",
        "\n",
        "* BFS can be used on both undirected and directed graphs.\n",
        "\n",
        "* BFS takes as input a graph $G$ and a start vertex $s$. It returns a distance list of each vertex from the start vertex and a list of parent of each vertex based on the BFS exploration.\n",
        "\n",
        "**Time and Space complexity**\n",
        "\n",
        "1. Initializing distance list - $O(n)$ time\n",
        "2. Initializing parent list - $O(n)$ time\n",
        "3. Setting s as start vertex - $O(1)$ time\n",
        "4. Processing a vertex and all of its edges - $O(1) + O(deg\n",
        "(u))$ time for vertex $u$ - so overall $O(n) + O(m)$ time\n",
        "\n",
        ">Running time of BFS is $O(n + m)$ for graphs with $n$ vertices and $m$ edges.\n",
        "\n",
        ">Space complexity is $O(n)$ since parent list, distance list each require $O(n)$ space and the queue can have size at most $n$.\n",
        "\n"
      ],
      "metadata": {
        "id": "6ijPL_PA8LeC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Time complexity analysis\n",
        "\n",
        "from collections import deque\n",
        "\n",
        "def bfs(G,s):\n",
        "  # number of vertices in G\n",
        "  n = len(G)             #O(1) time, O(1) space\n",
        "\n",
        "  # initialize dist to be max possible distance\n",
        "  dist = [float('inf')]*len(G)      #O(n) time, O(n) space\n",
        "\n",
        "  # noting down the parent of each vertex\n",
        "  parent = [None]*len(G)     #O(n) time, O(n) space\n",
        "\n",
        "  # start the search from s\n",
        "  dist[s] = 0               #O(1) time\n",
        "  queue = deque()           #O(1) time\n",
        "  queue.append(s)               #O(1) time\n",
        "\n",
        "  # start exploring\n",
        "  while len(queue) != 0:       #executes n times - once per vertex\n",
        "    # remove first element in the queue and explore its neighbors\n",
        "    u = queue.popleft()        #O(1) time since we used deque implementation\n",
        "    for v in G[u]:             #executes deg(u) many times\n",
        "      if dist[v] == float('inf'):\n",
        "        # v has not been visited\n",
        "        dist[v] = dist[u] + 1   #O(1) time\n",
        "        parent[v] = u           #O(1) time\n",
        "        queue.append(v)         #O(1) time\n",
        "\n",
        "  # return the list distance storing the shortest distance to each vertex\n",
        "  return dist, parent\n",
        "\n",
        "\n",
        "print(bfs([[1,2],[0,2],[0,1,3],[2]],0))\n",
        "print(bfs([[1,2],[0,2],[0,1],[4],[3]],0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8492de26-1492-4db9-9f80-e33cd4827a6e",
        "id": "0Fx2Vduj8tNE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "([0, 1, 1, 2], [None, 0, 0, 2])\n",
            "([0, 1, 1, inf, inf], [None, 0, 0, None, None])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BFS observations\n",
        "\n",
        "1.   Shortest paths discovered - BFS exploration strategy ensures that the shortest path from the start vertex $s$ is discovered for each of the other vertices that are reachable from $s$.\n",
        "\n",
        "2.  Unreachable vertices identified - Vertices that are not reachable from the start vertex have distance ` inf ` at the end of the execution of BFS.\n",
        "\n",
        "\n",
        "3.   Linear time - The time complexity of BFS is the same as that of DFS. Since each vertex is pushed to the queue only once, its edges are processed exactly once. The time spent per edge is $O(1)$ (setting distance, parent and adding neighbor to queue).\n",
        "\n",
        "Since the distance and parent lists require an initiation time of $O(|V|)$, the total time taken is $O(n + m)$.\n",
        "\n"
      ],
      "metadata": {
        "id": "pFxHT-rsnbqm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Queues\n",
        "\n",
        "A queue is a collection of objects that operates on the First In First Out principle (FIFO) - the first object in is the first object out.\n",
        "\n",
        "Queues are typically used for task scheduling - printers that receive multiple requests execute jobs in a first in first out manner. Printers use queues to maintain task requests. Keyboard buffers use a queue to prevent loss of typed data during lags - and since this data must appear in the sequence in which it was typed, buffers use queues.\n",
        "\n",
        "Queue operations visualization - https://visualgo.net/en/list\n"
      ],
      "metadata": {
        "id": "-dF7bHnAa56J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Queues - Python Implementation**\n",
        "\n",
        "\n",
        "\n",
        "1.   Queues in python can be implemented using lists.\n",
        "2.   Adding an element - queue.append can be used to add an element to the end of the queue\n",
        "3.   Removing an element - The elment at the front of the queue can be removed by using queue.popleft()\n",
        "4.  Size of the queue - Since python lists store length, one can use len(queue) to find out the number of elements in the queue.\n",
        "\n",
        "\n",
        "Reference - https://www.w3schools.com/python/python_dsa_queues.asp\n",
        "\n",
        "> Play around with the queue data structure using the code below to better understand how to work with queues.\n",
        "\n"
      ],
      "metadata": {
        "id": "I0FYZElknfdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Queues\n",
        "from collections import deque\n",
        "\n",
        "queue = deque()\n",
        "\n",
        "# Enqueue\n",
        "queue.append('A')\n",
        "queue.append('B')\n",
        "queue.append('C')\n",
        "print(\"Queue: \", queue)\n",
        "\n",
        "# Peek\n",
        "frontElement = queue[0]\n",
        "print(\"Peek: \", frontElement)\n",
        "\n",
        "# Dequeue - removing an element from the queue removes the earliest element\n",
        "poppedElement = queue.popleft()\n",
        "print(\"Dequeue: \", poppedElement)\n",
        "\n",
        "print(\"Queue after Dequeue: \", queue)\n",
        "\n",
        "# Enqueue - adding an element to the queue adds it at the end\n",
        "queue.append('D')\n",
        "print(\"Queue after adding D : \", queue)\n",
        "print(queue.popleft())\n",
        "print(\"Queue after dequeue : \", queue)\n",
        "\n",
        "# Size\n",
        "print(\"Size: \", len(queue))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyvYppoIcjdA",
        "outputId": "2bf33051-879c-4979-e493-be08bf175306"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Queue:  deque(['A', 'B', 'C'])\n",
            "Peek:  A\n",
            "Dequeue:  A\n",
            "Queue after Dequeue:  deque(['B', 'C'])\n",
            "Queue after adding D :  deque(['B', 'C', 'D'])\n",
            "B\n",
            "Queue after dequeue :  deque(['C', 'D'])\n",
            "Size:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Queue operations (deque implementation)\n",
        "\n",
        "* Enqueue\n",
        "\n",
        "Adding an element to the end of the queue - queue.append(x) [$O(1)$ time]\n",
        "\n",
        "* Dequeue\n",
        "\n",
        "Popping an element from the start of the queue - queue.popleft() [$O(1)$ time]\n",
        "\n",
        ">Deque (pronounced 'Deck') stands for double ended queue and is an abstract data type that supports accessing the first as well as the last element.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4xHlPI5RM9LR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BFS : Applications\n",
        "\n",
        "\n",
        "\n",
        "*   Shortest paths - BFS helps find shortest paths from $s$ to all other vertices.\n",
        "*   Finding \"close\" vertices - BFS can be used to identify all vertices that are close to $s$ - closeness measured in terms of number of edges needed to reach a vertex.\n",
        "* Graph exploration - Like DFS, BFS uses linear time to explore the connectivity structure of a graph.\n",
        "* Finding paths - Like DFS, BFS helps identify whether there exists any path from one vertex to another.\n",
        "* Cycle Detection - BFS can be used to detect the existence of cycles.\n",
        "\n"
      ],
      "metadata": {
        "id": "7n0_-hqS-emP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Establishing $s$-$t$ connectivity in $G$\n",
        "\n",
        "Input : Graph $G = (V,E)$ given in the adjacency list format, vertices $s,t$\n",
        "\n",
        "Output : True if there is some path from $s$ to $t$ in $G$, False otherwise\n",
        "\n"
      ],
      "metadata": {
        "id": "Jsh80dbxj8rM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# s-t Connectivity test\n",
        "\n",
        "# Idea - Since BFS(G,s) explores the graph starting with vertex s, make this function call.\n",
        "# At the end of its execution, any vertex reachable from s will have distance < inf.\n",
        "# All vertices not reachable from s have distance inf.\n",
        "\n",
        "# code -\n",
        "\n",
        "from collections import deque\n",
        "\n",
        "def connectivity(G,s,t):\n",
        "  distance, parent = bfs(G,s)\n",
        "\n",
        "  if(distance[t] < float('inf')):\n",
        "    return True\n",
        "\n",
        "  else:\n",
        "    return False\n",
        "\n",
        "\n",
        "def bfs(G,s):\n",
        "  # number of vertices in G\n",
        "  n = len(G)\n",
        "\n",
        "  # initialize dist to be max possible distance\n",
        "  dist = [float('inf')]*len(G)\n",
        "\n",
        "  # noting down the parent of each vertex\n",
        "  parent = [None]*len(G)\n",
        "\n",
        "  # start the search from s\n",
        "  dist[s] = 0\n",
        "  queue = deque()\n",
        "  queue.append(s)\n",
        "\n",
        "  # start exploring\n",
        "  while len(queue) != 0:\n",
        "    # remove first element in the queue and explore its neighbors\n",
        "    u = queue.popleft()    #pops leftmost element in queue (earliest added element)\n",
        "    for v in G[u]:\n",
        "      if dist[v] == float('inf'):\n",
        "        # v has not been visited\n",
        "        dist[v] = dist[u] + 1\n",
        "        parent[v] = u\n",
        "        queue.append(v)\n",
        "\n",
        "  # return the list distance storing the shortest distance to each vertex\n",
        "  return dist,parent\n",
        "\n",
        "\n",
        "print(connectivity([[1,2],[0,2],[0,1,3],[2]],0,3))\n",
        "print(connectivity([[1,2],[0,3,4,5],[0],[1,5,6],[1,6,7],[1,3],[3,4],[4]],0,4))\n",
        "print(connectivity([[1,2],[0,2],[0,1],[4],[3]],3,1))\n",
        "\n"
      ],
      "metadata": {
        "id": "mDTnstC4jkyB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33cdf877-86b0-46ee-d602-07ec42e2e00b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Shortest $s$-$t$ path in G\n",
        "\n",
        "Input - Graph $G = (V,E)$ given in the adjacency list format, vertices $s,t$\n",
        "\n",
        "Output - Some shortest $s$-$t$ path (in the form of a list) if it exists, the empty list otherwise"
      ],
      "metadata": {
        "id": "yKir9iYZgC7o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# shortest s-t path\n",
        "\n",
        "# Idea - BFS(G,s) discovers vertex t (if it is reachable from s) through a shortest s-t path in G due to the level order traversal\n",
        "# We can use the parent list to identify the path from s to t - working backwards from t as follows\n",
        "# t -> parent(t) -> parent(parent(t)) -> ... -> s\n",
        "# We can store this vertex sequence and reverse it to reveal the path from s to t.\n",
        "\n",
        "from collections import deque\n",
        "\n",
        "def shortest_path(G,s,t):\n",
        "   distance, parent = bfs(G,s)\n",
        "\n",
        "   if(distance[t] == 'inf'):\n",
        "     return []\n",
        "\n",
        "   else:\n",
        "     #identifying the shortest s-t path\n",
        "     shortest = []\n",
        "     v = t\n",
        "     while(v != s):\n",
        "       shortest.append(v)\n",
        "       v = parent[v]\n",
        "\n",
        "     shortest.append(s)\n",
        "     shortest.reverse()\n",
        "     return shortest\n",
        "\n",
        "def bfs(G,s):\n",
        "  # number of vertices in G\n",
        "  n = len(G)\n",
        "\n",
        "  # initialize dist to be max possible distance\n",
        "  dist = [float('inf')]*len(G)\n",
        "\n",
        "  # noting down the parent of each vertex\n",
        "  parent = [None]*len(G)\n",
        "\n",
        "  # start the search from s\n",
        "  dist[s] = 0\n",
        "  queue = deque()\n",
        "  queue.append(s)\n",
        "\n",
        "  # start exploring\n",
        "  while len(queue) != 0:\n",
        "    # remove first element in the queue and explore its neighbors\n",
        "    u = queue.popleft()    #pops leftmost element in queue (earliest added element)\n",
        "    for v in G[u]:\n",
        "      if dist[v] == float('inf'):\n",
        "        # v has not been visited\n",
        "        dist[v] = dist[u] + 1\n",
        "        parent[v] = u\n",
        "        queue.append(v)\n",
        "    # return the list distance storing the shortest distance to each vertex\n",
        "  return dist,parent\n",
        "\n",
        "print(shortest_path([[1,2],[0,2],[0,1,3],[2]],0,2))\n",
        "print(shortest_path([[1,2],[0,2],[0,1,3],[2]],0,3))\n",
        "print(shortest_path([[1,2],[0,3,4,5],[0],[1,5,6],[1,6,7],[1,3],[3,4],[4]],0,4))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xZIl_UWep3M",
        "outputId": "a6c81d7a-4001-4831-a51a-13b4855ab3f7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 2]\n",
            "[0, 2, 3]\n",
            "[0, 1, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finding 2nd degree connections\n",
        "\n",
        "Input - Graph $G = (V,E)$ given in the adjacency list format, vertex $s$\n",
        "\n",
        "Output - A list of all vertices that are 2nd degree connections of $s$ (that is, they are a neighbor of some neighbor of $s$)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nsRI34xgkGAf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2nd degree connections of s\n",
        "\n",
        "# Main idea - 2nd degree connections of s are precisely those vertices that are at distance 2 from s.\n",
        "# We can use BFS(G,s) to identify all distances from s and then create a list with those at distance 2.\n",
        "\n",
        "from collections import deque\n",
        "\n",
        "def second_degree(G,s):\n",
        "  distance, parent = bfs(G,s)\n",
        "\n",
        "  second_connections = []\n",
        "\n",
        "  for i in range(len(G)):\n",
        "    if(distance[i] == 2):\n",
        "      second_connections.append(i)\n",
        "\n",
        "  return second_connections\n",
        "\n",
        "def bfs(G,s):\n",
        "  # number of vertices in G\n",
        "  n = len(G)\n",
        "\n",
        "  # initialize dist to be max possible distance\n",
        "  dist = [float('inf')]*len(G)\n",
        "\n",
        "  # noting down the parent of each vertex\n",
        "  parent = [None]*len(G)\n",
        "\n",
        "  # start the search from s\n",
        "  dist[s] = 0\n",
        "  queue = deque()\n",
        "  queue.append(s)\n",
        "\n",
        "  # start exploring\n",
        "  while len(queue) != 0:\n",
        "    # remove first element in the queue and explore its neighbors\n",
        "    u = queue.popleft()    #pops leftmost element in queue (earliest added element)\n",
        "    for v in G[u]:\n",
        "      if dist[v] == float('inf'):\n",
        "        # v has not been visited\n",
        "        dist[v] = dist[u] + 1\n",
        "        parent[v] = u\n",
        "        queue.append(v)\n",
        "    # return the list distance storing the shortest distance to each vertex\n",
        "  return dist,parent\n",
        "\n",
        "print(second_degree([[1,2],[0,2],[0,1,3],[2]],0))\n",
        "print(second_degree([[1,2],[0,3,4,5],[0],[1,5,6],[1,6,7],[1,3],[3,4],[4]],2))\n",
        "print(second_degree([[1,2],[0,3,4,5],[0],[1,5,6],[1,6,7],[1,3],[3,4],[4]],0))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2JjpAQake_L",
        "outputId": "61f3d11b-4736-4589-edb8-d52296e0f2d9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3]\n",
            "[1]\n",
            "[3, 4, 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BFS on directed graphs\n",
        "\n",
        "1. Running BFS(G,s) on a directed graph G will visit all vertices reachable from s in G.\n",
        "\n",
        "2. For each of these vertices, BFS(G,s) finds the distance of the shortest path from s.\n",
        "\n",
        "3. We can use the parent list to identify the shortest path from s to any vertex x that is reachble from s in G.\n"
      ],
      "metadata": {
        "id": "4GELQu2sP_8N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cheapest flight route\n",
        "\n",
        "Consider the flight route network problem. Suppose you are trying to find the cheapest option to go from Chicago to San Francisco.\n",
        "\n",
        "The following diagram has the costs of direct flights across cities -\n",
        "Chicago, New York City, San Diego, Dallas, San Francisco\n",
        "\n",
        "![Screenshot 2026-03-25 at 2.30.41 PM.png](data:image/png;base64,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)\n",
        "\n",
        "> What is the shortest path from Chicago to San Francisco?\n",
        "\n",
        "> What is the cheapest cost to travel from Chicago to San Francisco on May 1?\n",
        "\n",
        "> What is the flight route that corresponds to the cheapest cost?\n"
      ],
      "metadata": {
        "id": "2P4_tuIve2DN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lab 8 (preview)\n",
        "\n",
        "Inheritance division vs Budget reporting\n",
        "\n",
        "We will see how DFS and BFS are uniquely suited to different ways of transmitting/collection information in hierarchical settings."
      ],
      "metadata": {
        "id": "OSnf2w-yspXU"
      }
    }
  ]
}