Choose clustering for social network users

Q: Choose clustering for social network users

This question evaluates competency in clustering algorithms and graph analytics within the Machine Learning domain, focusing on grouping users from feature vectors and social network graphs.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

Scenario

You need to cluster users to discover meaningful groups (e.g., communities, interest groups, or usage segments). You may have:

Traditional tabular features per user (usage frequency, demographics, embeddings, etc.), and/or
A social network graph (nodes = users, edges = friendships/follows/messages).

Questions

What clustering algorithm(s) would you consider, and why?
What are the key differences between traditional clustering (feature-vector based) and social network / graph clustering ?
How would you evaluate cluster quality and choose the number of clusters?
What practical issues arise at scale (millions of users), and how would you handle them?

Choose clustering for social network users

Scenario

Questions

Solution

Comments (0)

Choose clustering for social network users

Overview

Scenario

Questions

Solution

Comments (0)