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TikTok·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

TikTok software engineering interview that centered on a graph theory problem. Pretty standard algorithmic round but the follow-up questions about edge cases and complexity analysis kept it from being a cakewalk.

Questions Asked (1)

Q1

You're given an undirected graph as an adjacency list where adj[i] contains the indices of guests who know guest i. Can all guests be seated in at most two groups such that no two guests in the same group know each other? Return true or false, implement the algorithm, handle disconnected components, and analyze time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is bipartite checking, which I recognized pretty fast, but I fumbled the disconnected components part initially.

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AI HintsAI Generated

Suggested Approach

Model the problem as checking if the graph is bipartite, since two groups with no internal edges correspond to a 2-coloring. Use BFS or DFS to color each connected component, ensuring no edge connects same-colored vertices. Return false if any conflict arises; otherwise true.

Pro tip: Emphasize that the graph may be disconnected, so you must iterate over all unvisited nodes to cover every component. Also, clarify that the adjacency list is undirected, so each edge appears twice, and handle it accordingly.

1. Clarify problem and constraints

Confirm that the graph is undirected, guests are vertices, and 'know each other' means an edge. The goal is to partition vertices into two independent sets.

2. Choose algorithm

Use BFS or DFS to attempt a 2-coloring of the graph. Initialize a color array with -1 (uncolored) and assign alternating colors to neighbors.

3. Handle disconnected components

Loop through all vertices; if a vertex is uncolored, start a new BFS/DFS from it, assigning it color 0. This ensures every component is checked.

4. Detect conflicts

During traversal, if a neighbor already has the same color as the current vertex, return false immediately. Otherwise, continue until all vertices are processed.

5. Analyze complexity

Time complexity is O(V + E) since each vertex and edge is visited once. Space complexity is O(V) for the color array and queue/stack, plus O(V + E) for the adjacency list if counted.

Key Points to Mention

  • Bipartite graph equivalence: two groups with no internal edges = 2-coloring.
  • Handling disconnected components by iterating over all vertices.
  • Using BFS or DFS for traversal and coloring.
  • Conflict detection: if neighbor has same color, return false.
  • Time complexity O(V + E) and space complexity O(V).
  • Edge cases: empty graph, single vertex, self-loops (if any), and multiple components.

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