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About this interview
A technical interview on Graph Neural Networks, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain the message-passing framework underlying GNNs
Compare GCN, GraphSAGE, and GAT
Describe applications (recommendation, fraud, molecules) and why graphs help
Explain over-smoothing and scalability challenges in GNNs
Topics covered
Motivation for graphsGraph representationGNN vs MLP on graphsMessage-passing frameworkNode embeddingsGCN architectureGraphSAGETask taxonomyGAT architectureTransductive vs inductiveLink predictionFraud detection applicationMolecular applicationsOver-smoothing
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through why graphs are a natural fit for data that has relational structure — and give me two or three concrete examples of domains where that matters?
“In a fraud detection system, how would you model the problem as a graph, and what kind of signals does a GNN capture that a standard tabular model would miss?
“What is cluster-GCN or the cluster-based mini-batching approach, and how does it differ from neighbor sampling in terms of variance and efficiency?