Implement a Graph-Based Risk Score Propagation
Last updated: December 9, 2025
Quick Overview
Given a security graph with weighted nodes and edges, implement an algorithm that propagates risk scores from high-risk nodes to connected nodes, accounting for edge weights and diminishing returns over distance.
Wiz
December 9, 20259
10
4,103 solved
Given a security graph with weighted nodes and edges, implement an algorithm that propagates risk scores from high-risk nodes to connected nodes, accounting for edge weights and diminishing returns over distance.
Risk score propagation is a core algorithm at Wiz. When a critical vulnerability is discovered on a node, connected nodes inherit some of that risk based on their relationship. A database directly accessible from a compromised VM inherits more risk than one separated by multiple network hops. This tests your ability to implement graph algorithms with practical constraints.
What the Interviewer Expects
- Implement a BFS-based propagation with decay factor
- Handle cycles without infinite loops
- Support different edge weight semantics
- Produce a final risk score for every reachable node
- Optimize for large graphs
Key Topics to Cover
How to Approach This
- Clarify input constraints and edge cases before writing code.
- Walk through your approach verbally and confirm with the interviewer before coding.
- Start with a brute force solution, then optimize. Mention time and space complexity.
- Test your solution with examples, including edge cases like empty input or duplicates.
- Consider common patterns: sliding window, two pointers, hash map, BFS/DFS, dynamic programming.
Possible Follow-up Questions
- How would you handle negative risk (a firewall reducing risk for nodes behind it)?
- Can you implement incremental updates when a single node's risk changes?
- How would you parallelize this for a graph with millions of nodes?
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Practice DSA ProblemsSample Answer
Problem Analysis
This problem is a graph traversal problem where we need to propagate risk scores through a weighted graph. The specific pattern that applies here is a modified BFS (Breadth-First Search) using a prior...
Approach
- Initialize Data Structures: Create a priority queue to store nodes along with their risk scores (using a tuple of (risk_score, node)). Also, maintain a dictionary to track the highest risk scor...
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