DFS Depth First Search on Graph in DSA Java - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind Depth First Search (DFS) on a graph?

DFS explores as far as possible along each branch before backtracking, visiting nodes deeply before moving to neighbors.

beginner

In DFS, what data structure is commonly used to keep track of nodes to visit next?

A stack is used either explicitly or via recursion call stack to keep track of nodes to visit next in DFS.

beginner

What is the purpose of a 'visited' array or set in DFS?

It prevents revisiting nodes, avoiding infinite loops and repeated work by marking nodes already explored.

intermediate

How does DFS differ from Breadth First Search (BFS) in graph traversal?

DFS goes deep into a branch before backtracking, while BFS explores all neighbors level by level.

intermediate

What is the time complexity of DFS on a graph with V vertices and E edges?

The time complexity is O(V + E) because each vertex and edge is visited once in DFS.

Which data structure is naturally used by recursive DFS calls?

A Queue

B Stack

C Heap

D HashMap

Recursive DFS uses the call stack to keep track of nodes, which behaves like a stack.

What happens if you don't mark nodes as visited in DFS on a graph with cycles?

A DFS will only visit nodes once

B DFS will stop immediately

C DFS will run infinitely or revisit nodes repeatedly

D DFS will convert to BFS

Without marking visited nodes, DFS can get stuck in cycles revisiting the same nodes endlessly.

In DFS, when do you backtrack from a node?

A After exploring all its neighbors

B Before exploring neighbors

C Immediately after visiting the node

D Only if the node has no neighbors

DFS backtracks after fully exploring all neighbors of the current node.

Which of these is NOT a typical use case of DFS?

A Finding shortest path in unweighted graph

B Topological sorting

C Finding connected components

D Detecting cycles

Shortest path in unweighted graphs is better found by BFS, not DFS.

What is the space complexity of DFS in the worst case for a graph with V vertices?

A O(log V)

B O(1)

C O(V + E)

D O(V)

DFS uses O(V) space for the visited array and recursion stack in worst case.

Explain how DFS explores nodes in a graph and how it uses the visited array.

Think about going down one path fully before trying others.

Describe the differences between DFS and BFS in graph traversal.

Compare how each searches through the graph.

Practice

1. What is the main idea behind Depth First Search (DFS) on a graph?

easy

A. Randomly visit nodes until all are visited

B. Visit all neighbors of a node before moving deeper

C. Visit nodes in order of their distance from the start node

D. Explore as far as possible along each branch before backtracking

Solution

1. Step 1: Understand DFS traversal

   DFS explores a graph by going deep into one branch before backtracking to explore other branches.

2. Step 2: Compare options with DFS behavior

   Explore as far as possible along each branch before backtracking matches this behavior exactly, while others describe BFS or incorrect methods.

3. Final Answer:

   Explore as far as possible along each branch before backtracking -> Option D

4. Quick Check:

   DFS = deep exploration first [OK]

Hint: DFS goes deep first, BFS goes wide first [OK]

Common Mistakes:

• Confusing DFS with BFS
• Thinking DFS visits neighbors first
• Assuming DFS visits nodes by distance

2. Which of the following is the correct way to start a DFS traversal from node 0 in Java using recursion?

easy

A. dfs(0, visited, graph);

B. DFS(visited, graph, 0);

C. dfs(graph, 0, visited);

D. dfs(visited, 0, graph);

Solution

1. Step 1: Identify typical DFS method signature

   Common DFS signature is dfs(currentNode, visitedArray, graph).

2. Step 2: Match options to this signature

   dfs(0, visited, graph); matches the order: node, visited, graph. Others have wrong parameter order.

3. Final Answer:

   dfs(0, visited, graph); -> Option A

4. Quick Check:

   Correct parameter order = dfs(node, visited, graph) [OK]

Hint: DFS method usually starts with node, visited, graph [OK]

Common Mistakes:

• Mixing parameter order
• Using uppercase method name 'DFS'
• Passing graph before node

3. Given the graph adjacency list: graph = {{1, 2}, {0, 3}, {0}, {1}} and starting DFS from node 0, what is the order of nodes visited?

medium

A. [0, 1, 3, 2]

B. [0, 2, 1, 3]

C. [0, 1, 2, 3]

D. [0, 3, 1, 2]

Solution

1. Step 1: Start DFS at node 0 and visit neighbors

   From 0, neighbors are 1 and 2. DFS visits 1 first (assuming adjacency order).

2. Step 2: From node 1, visit its neighbors

   Neighbors of 1 are 0 (visited) and 3. Visit 3 next.

3. Step 3: Backtrack and visit remaining neighbors

   After 3, backtrack to 0 and visit 2.

4. Final Answer:

   [0, 1, 3, 2] -> Option A

5. Quick Check:

   DFS order = 0,1,3,2 [OK]

Hint: Follow neighbors deeply in adjacency order [OK]

Common Mistakes:

• Visiting neighbors in wrong order
• Not backtracking correctly
• Confusing BFS order with DFS

4. What is wrong with this DFS code snippet in Java?

void dfs(int node, boolean[] visited, List<Integer>[] graph) {
  visited[node] = true;
  for (int neighbor : graph[node]) {
    if (visited[neighbor]) {
      dfs(neighbor, visited, graph);
    }
  }
}

medium

A. It does not mark nodes as visited

B. It calls dfs on already visited neighbors causing infinite recursion

C. It uses wrong data types for graph

D. It misses the base case for recursion

Solution

1. Step 1: Check visited condition before recursive call

   The code calls dfs(neighbor) only if visited[neighbor] is true, which means already visited.

2. Step 2: Identify the problem caused

   This causes infinite recursion because it revisits nodes already visited instead of skipping them.

3. Final Answer:

   It calls dfs on already visited neighbors causing infinite recursion -> Option B

4. Quick Check:

   Call dfs only if neighbor not visited [OK]

Hint: Call dfs only if neighbor is NOT visited [OK]

Common Mistakes:

• Calling dfs on visited nodes
• Forgetting to mark visited nodes
• Incorrect loop over neighbors

5. You want to find if there is a path between node 0 and node 4 in this graph:

graph = {{1, 2}, {0, 3}, {0, 4}, {1}, {2}}

Which DFS approach correctly determines if node 4 is reachable from node 0?

hard

A. Run DFS from 4, stop and return true if node 0 is found during traversal

B. Run DFS from 0 and count all visited nodes without checking for 4

C. Run DFS from 0, stop and return true if node 4 is found during traversal

D. Run DFS from 0 but skip node 2 to avoid cycles

Solution

1. Step 1: Understand the goal

   We want to check if node 4 is reachable from node 0, so start DFS at 0.

2. Step 2: Use DFS to find target node

   During DFS, if node 4 is found, stop and return true immediately.

3. Step 3: Evaluate options

   Run DFS from 0, stop and return true if node 4 is found during traversal matches this approach. Run DFS from 4, stop and return true if node 0 is found during traversal starts from wrong node. Run DFS from 0 and count all visited nodes without checking for 4 ignores target. Run DFS from 0 but skip node 2 to avoid cycles skips nodes incorrectly.

4. Final Answer:

   Run DFS from 0, stop and return true if node 4 is found during traversal -> Option C

5. Quick Check:

   DFS from start, stop on target found [OK]

Hint: Stop DFS early when target node is found [OK]

Common Mistakes:

• Starting DFS from wrong node
• Not checking for target during traversal
• Skipping nodes that are part of path