← Bytedance Interview Insights

Bytedance·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Bytedance SWE interview with a graph/connectivity problem that looks deceptively simple on the surface. The core challenge was recognizing the transitive nature of the similarity relationship and modeling it correctly.

Questions Asked (1)

Q1

Given an N x N boolean matrix where a 1 at position [i][j] means photo i and photo j are directly similar, and similarity is transitive, return the total number of distinct photo groups.

Algorithms & Data Structures
Author's notes

Took me a beat longer than it should have to see this was just connected components.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model the matrix as an undirected graph where each photo is a node and a 1 at [i][j] indicates an edge. The number of distinct photo groups equals the number of connected components in this graph. Use Union-Find (Disjoint Set Union) to efficiently merge similar photos and count components, or BFS/DFS if the graph is dense.

Pro tip: Clarify whether the matrix is symmetric and whether self-similarity (diagonal) is always true; this affects initialization. Also, mention that Union-Find with path compression and union by rank gives near O(N^2) time, which is optimal for reading the matrix.

1. Understand the problem and clarify assumptions

Confirm that similarity is transitive, so groups are connected components. Ask if the matrix is symmetric and if diagonal entries are always 1.

2. Choose the right data structure

Select Union-Find for efficient merging and component counting, or BFS/DFS if the graph is sparse. Discuss trade-offs.

3. Initialize and process the matrix

Initialize each photo as its own group. Iterate through the upper triangle of the matrix (i < j) and union i and j when matrix[i][j] is 1.

4. Count distinct groups

After processing all pairs, count the number of unique roots in the Union-Find structure. This is the number of connected components.

5. Analyze complexity and edge cases

Time complexity is O(N^2) due to matrix traversal, with near O(1) per union/find. Space is O(N). Handle edge cases like N=0 or all zeros.

Key Points to Mention

  • Graph representation: photos as nodes, similarities as edges
  • Connected components as photo groups
  • Union-Find (Disjoint Set Union) with path compression and union by rank
  • Time complexity O(N^2) and space complexity O(N)
  • Alternative BFS/DFS approach and when it might be preferable
  • Edge cases: empty matrix, all zeros, all ones, non-symmetric matrix

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