Depth-first Search

JSTS

Write a function that implements the depth-first search algorithm on a directed graph (in adjacency list format), given a starting node.

Examples

const graph1 = {
  A: ['B', 'C', 'D'],
  B: ['E', 'F'],
  C: ['G', 'H'],
  D: ['I', 'J'],
  E: ['D'],
  F: [],
  G: [],
  H: [],
  I: [],
  J: [],
};
depthFirstSearch(graph1, 'A'); // ['A', 'B', 'E', 'D', 'I', 'J', 'F', 'C', 'G', 'H']
depthFirstSearch(graph1, 'B'); // ['B', 'E', 'D', 'I', 'J', 'F']

const graph2 = {
  A: ['B', 'C'],
  B: ['D', 'E'],
  C: ['F', 'G'],
  D: [],
  E: [],
  F: [],
  G: [],
};
depthFirstSearch(graph2, 'A'); // ['A', 'B', 'D', 'E', 'C', 'F', 'G']
depthFirstSearch(graph2, 'E'); // ['E']

Recap (Hint)

Depth-first search (DFS) is an algorithm used for traversing a graph or a tree. The output from DFS is an array of the graph's nodes in the order they were traversed. This output is useful for a variety of different use cases and purposes, which makes DFS a useful algorithm to know. Some use cases:

Find a specific node or group of nodes. This is common in front end to find specific DOM node(s) within the DOM tree.
Checking if a graph is connected.
Finding a path between two nodes in a graph.
Generating a topological sort of a directed acyclic graph (DAG).
Identifying cycles in a graph.
As a building block for other algorithms.

Here is an overview of how DFS works to traverse a graph, using the standard implementation that takes in an adjacency list (we use an array instead) and the root node:

Initialize an array or a stack to store nodes to be visited. Push root node.
Initialize a set to track visited nodes.
Enter a loop that continues until the stack is empty. In each iteration of the loop:
1. Pop the top node from the array / stack.
2. Retrieve the neighbors of the node from the input graph.
3. For each neighbor, check if it has been visited. If it has not been visited, add it to the set of nodes to be visited.
Return the set of visited nodes.

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Depth-first Search

JSTS

Write a function that implements the depth-first search algorithm on a directed graph (in adjacency list format), given a starting node.

Examples

const graph1 = {
  A: ['B', 'C', 'D'],
  B: ['E', 'F'],
  C: ['G', 'H'],
  D: ['I', 'J'],
  E: ['D'],
  F: [],
  G: [],
  H: [],
  I: [],
  J: [],
};
depthFirstSearch(graph1, 'A'); // ['A', 'B', 'E', 'D', 'I', 'J', 'F', 'C', 'G', 'H']
depthFirstSearch(graph1, 'B'); // ['B', 'E', 'D', 'I', 'J', 'F']

const graph2 = {
  A: ['B', 'C'],
  B: ['D', 'E'],
  C: ['F', 'G'],
  D: [],
  E: [],
  F: [],
  G: [],
};
depthFirstSearch(graph2, 'A'); // ['A', 'B', 'D', 'E', 'C', 'F', 'G']
depthFirstSearch(graph2, 'E'); // ['E']

Recap (Hint)

Find a specific node or group of nodes. This is common in front end to find specific DOM node(s) within the DOM tree.
Checking if a graph is connected.
Finding a path between two nodes in a graph.
Generating a topological sort of a directed acyclic graph (DAG).
Identifying cycles in a graph.
As a building block for other algorithms.

Here is an overview of how DFS works to traverse a graph, using the standard implementation that takes in an adjacency list (we use an array instead) and the root node:

Initialize an array or a stack to store nodes to be visited. Push root node.
Initialize a set to track visited nodes.
Enter a loop that continues until the stack is empty. In each iteration of the loop:
1. Pop the top node from the array / stack.
2. Retrieve the neighbors of the node from the input graph.
3. For each neighbor, check if it has been visited. If it has not been visited, add it to the set of nodes to be visited.
Return the set of visited nodes.

Similar Questions

Breadth-first Search

Clarification Questions

If unspecified:

Order of traversal: Should we use a standard pre-order traversal or other orders of traversal?
Input format: What is the format of the input data? Can we assume the input will be an adjacency list in the form of a JavaScript Object where keys are nodes and values are child nodes?
Output format: How should the output be formatted? Are there specific requirements or can we return an array with elements in the order of traversal?
Disconnected graphs: Do we have to consider or handle the case for disconnected graphs where there are multiple subgraphs that are not connected to each other?

Solution

The solution implements the algorithm outlined in the description.

/**
 * @param {Record<string, Array<string>} graph The adjacency list representing the graph.
 * @param {string} source The source node to start traversal from. Has to be a valid node if graph is non-empty.
 * @return {string[]} A DFS-traversed order of nodes.
 */
export default function depthFirstSearch(graph, source) {
  // If there are no nodes in the graph, just return an empty array
  if (Object.keys(graph).length === 0) {
    return [];
  }

  // Create an stack to store the nodes to be visited. We can simulate
  // stacks using arrays in JavaScript.
  // Add the root node since we're doing a pre-order DFS.
  const toBeVisited = [];
  toBeVisited.push(source);

  // Initialize a set that tracks visited nodes.
  const visited = new Set();

  // Loop as long as array is empty (i.e. there are still nodes to be visited).
  while (toBeVisited.length !== 0) {
    // Pop top node from array (toBeVisited) and add it to the set (visited).
    const node = toBeVisited.pop();
    visited.add(node);

    // Retrieve neighbors (values of the adjacency list input Object)
    const neighbors = graph[node];
    // Push neighbors, in reverse order, onto array to be visited
    // to preserve original order of neighbors when visiting (popping off the array).
    for (let i = neighbors.length - 1; i >= 0; i--) {
      const neighbor = neighbors[i];
      // First check if the neighbor has already been visited before adding it.
      if (!visited.has(neighbor)) {
        toBeVisited.push(neighbor);
      }
    }
  }

  // The visited nodes is the traversal order.
  return Array.from(visited);
}

We can also perform DFS recursively, which is can be more intuitive in certain cases. The recursion call stack is an implicit stack to track which nodes to visit next.

/**
 * @param {Object} graph Node to array of neighboring nodes.
 * @param {string} source Source node to start traversal from. Has to be a valid node if graph is non-empty.
 * @return {string[]} A DFS-traversed order of nodes.
 */
export default function depthFirstSearch(graph, source) {
  // If there are no nodes in the graph, just return an empty array
  if (Object.keys(graph).length === 0) {
    return [];
  }

  // Initialize a set that tracks visited nodes.
  const visited = new Set();

  function traverse(node) {
    // Visited before, we can ignore.
    if (visited.has(node)) {
      return;
    }

    visited.add(node);
    // Recursively visit each neighbor.
    graph[node].forEach((neighbor) => {
      traverse(neighbor);
    });
  }

  // Start traversing from the source.
  traverse(source);

  // The visited nodes is the traversal order.
  return Array.from(visited);
}

Edge Cases

Empty graphs: Return an empty array without crashing.
Graphs with only one-two nodes: Traverse without crashing.
Cyclic graphs: Make sure not to traverse visited nodes again.
Disjoint graphs: Doesn't require special handling but good to be aware of.

Depth-first Search

Examples

Recap (Hint)

Depth-first Search

Examples

Recap (Hint)

Similar Questions

Clarification Questions

Solution

Edge Cases

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