Practice Graph Neural Networks (gnns) (11.7.2) - Representation Learning & Structured Prediction
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Graph Neural Networks (GNNs)

Practice - Graph Neural Networks (GNNs)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does GNN stand for?

💡 Hint: Think about the type of data these networks are designed for.

Question 2 Easy

Name the two fundamental components of graph data.

💡 Hint: What makes up the structure of a graph?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What do Graph Neural Networks primarily work on?

Grid-like data
Graph data
Time series data

💡 Hint: Consider the type of structure GNNs are built to interpret.

Question 2

True or False: GNNs only model nodes without considering the edges.

True
False

💡 Hint: Think about the interactions within graphs.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a set of user interactions in a social network graph, how would you implement a GNN to predict friend suggestions? Outline the key steps.

💡 Hint: Think about how information flows in a network.

Challenge 2 Hard

How would you adapt a GNN model to account for varying edge weights in a graph? Discuss your approach.

💡 Hint: Consider the implications of different strengths in relationships.

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Reference links

Supplementary resources to enhance your learning experience.