Interview: 'when should you use graph-based methods over tabular?'
mediumAnswer
- (1) Data has natural relational structure (users↔items, molecules, transactions, social nets).
- (2) Prediction depends on neighborhood (link prediction, fraud rings, drug interactions).
- (3) Rich metadata on nodes + edges.
- (4) You want to explain via relational features (visible connections).
- Don't use graphs when: (1) data is purely tabular independent samples, (2) no clear notion of relation, (3) added complexity outweighs marginal gains.
Check yourself — multiple choice
- Random
- Use graphs when: natural relations + neighborhood-dependent prediction + rich node/edge metadata + explainable relations; skip when data is purely tabular + no relation
- Always
- Never
Use graphs when data has natural relations + neighborhood-dependent signal.
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Practise Unsupervised Learning
214 interview questions in this topic.
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