Breadth-First Search Questions at Airbnb: What to Expect

Breadth-First Search (BFS) is a core algorithm for traversing graphs and trees level by level, making it essential for problems involving shortest paths, level-order processing, or exploring states in layers. At Airbnb, with 9 out of 64 coding questions tagged with BFS, it's a frequent topic in interviews. The company's product domain—connecting hosts and guests across a global network of listings, experiences, and destinations—naturally maps to graph problems. Think of user connections, location-based searches, reservation calendars, or routing between services. BFS is the go-to tool for finding the minimum steps or distance in such unweighted scenarios, whether you're navigating a grid of cities, a social graph, or a state space in a system design question. Mastering it demonstrates you can handle real-world spatial and relational data efficiently.

What to Expect — Types of Problems

Airbnb's BFS questions typically fall into three categories:

1. Grid and Matrix Traversal: Problems where you navigate a 2D grid (like a map or floor plan) to find the shortest path, count reachable cells, or spread influence. Examples include "Walls and Gates" style problems or finding the nearest service point.
2. Tree Level-Order Traversal: While straightforward, variations appear, such as zigzag traversal, connecting level siblings, or finding the largest value in each row of a binary tree.
3. Shortest Path in an Unweighted Graph: This is the most common and critical type. You'll model a scenario (like transforming one word to another through a dictionary, or the classic "Minimum Knight Moves") as a graph where each node is a state, and edges are valid transitions. BFS guarantees the shortest number of steps.

Expect the problems to have a practical twist—instead of abstract graphs, you might be given a list of user IDs and friendships, or a set of rules for changing a reservation date. The key is to quickly identify the underlying graph structure.

How to Prepare — Study Tips with One Code Example

Focus on the pattern, not memorization. The standard BFS template uses a queue and a visited set. Practice writing it from scratch until it's automatic.

Key Pattern: BFS for Shortest Path in an Unweighted Graph The core idea is to explore all neighbors of the current node before moving deeper. This guarantees that when you first reach a target node, you've done so in the minimum number of steps.

from collections import deque

def bfs_shortest_path(start, target, get_neighbors):
    if start == target:
        return 0
    queue = deque([start])
    visited = {start}
    steps = 0

    while queue:
        for _ in range(len(queue)):
            current = queue.popleft()
            if current == target:
                return steps
            for neighbor in get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append(neighbor)
        steps += 1
    return -1  # Target not reachable

Study Tips:

• Internalize this template. The get_neighbors function is where problem-specific logic lives.
• Practice converting problem descriptions into a graph: What is a node? What defines an edge?
• Always handle cycles with a visited set to avoid infinite loops.
• For grid problems, the neighbors are often the four (or eight) directional moves.

Recommended Practice Order

1. Fundamentals: Start with pure BFS on explicit graphs and trees (Binary Tree Level Order Traversal).
2. Grids: Practice classic LeetCode problems like "Number of Islands" (BFS version) and "Rotting Oranges."
3. Shortest Path Transformations: Solve "Word Ladder" and "Minimum Genetic Mutation"—these are highly representative of Airbnb's problem style.
4. Airbnb-Tagged Problems: Finally, work through the 9 Airbnb-tagged BFS questions on platforms like LeetCode. This will expose you to the exact difficulty and potential variations they use.

Practice Breadth-First Search at Airbnb