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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google SWE coding round, one question on BFS-based shortest path finding. Pretty standard graph traversal stuff but there was a follow-up about optimizing graph construction that I wasn't fully ready for.

Questions Asked (1)

Q1

Given a graph as an adjacency list, a start node, and a target node, implement BFS to find the shortest path between them. Also discuss how you'd optimize the graph construction step to avoid a naive approach.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I got the BFS part down pretty quickly, tracking parents to reconstruct the path at the end.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the graph representation and edge cases, then implement BFS with a queue and parent tracking to reconstruct the shortest path. For optimization, discuss preprocessing the adjacency list to remove duplicates, use efficient data structures, and consider lazy evaluation or on-the-fly graph construction if applicable.

Pro tip: Mention that BFS finds the shortest path in unweighted graphs, and if the graph is weighted, Dijkstra's algorithm is needed. Also, highlight the importance of bidirectional BFS for large graphs to reduce search space.

1. Clarify requirements and edge cases

Ask about graph size, directed/undirected, weighted/unweighted, and whether the graph is static or dynamic. Discuss handling of disconnected nodes, cycles, and self-loops.

2. Implement BFS for shortest path

Use a queue for BFS, a visited set to avoid cycles, and a parent map to reconstruct the path. Return the path from start to target if found, else indicate no path.

3. Optimize graph construction

Avoid naive O(V^2) adjacency matrix; use adjacency lists. For building, consider deduplicating edges, using hash sets for O(1) lookups, and parallelizing if large.

4. Discuss advanced optimizations

Mention bidirectional BFS to reduce time and space, early termination when target found, and using arrays instead of hash maps for dense graphs.

5. Analyze complexity and trade-offs

State time and space complexity: O(V+E) for BFS, and for construction O(E) with adjacency list. Discuss trade-offs between preprocessing and query time.

Key Points to Mention

  • BFS guarantees shortest path in unweighted graphs; for weighted, use Dijkstra.
  • Use a queue (FIFO) and track visited nodes to avoid infinite loops.
  • Reconstruct path using a parent map or by storing paths in the queue.
  • Optimize graph construction by using adjacency lists, deduplicating edges, and considering lazy loading.
  • Bidirectional BFS can significantly reduce search space for large graphs.
  • Time complexity: O(V+E) for BFS; space complexity: O(V) for visited and queue.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.