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Predictions

About this module

In this module you will learn how to build a Machine Learning classifier to predict co-authorships in the citation graph.

At the end of this module, you should be able to:

  • Describe what link prediction is.
  • Use the link prediction graph algorithms in Neo4j.
  • Understand the challenges when building Machine Learning models on graph data.
  • Build a link prediction classifier using scikit-learn with features derived from the Neo4j Graph Data Science library.

Exercise 2: Building a binary classifier

In this exercise, you will build a binary classifier to predict co-authorships using a notebook.

Launch the 04_Predictions.ipynb notebook and follow the steps in this exercise.

Check your understanding

Question 1

Which Link Prediction algorithm “captures the notion that two strangers who have a common friend may be introduced by that friend.”?

Select the correct answer.

  • Adamic Adar
  • Common Neighbors
  • PageRank
  • Preferential Attachment

Question 2

Which of these challenges do we need to address when building a binary classifier for Link Prediction?

Select the correct answers.

  • Class Imbalance
  • Clustering cut-off
  • Data Leakage
  • Damping factor

Question 3

Which feature is the most important in our final model?

Select the correct answer.

  • Preferential Attachment
  • Triangles (min)
  • Common neighbors
  • Louvain

Summary

You should now be able to:

  • Describe what Link Prediction is.
  • Use the Link Prediction algorithms in Neo4j.
  • Understand the challenges when building Machine Learning models on graph data.
  • Build a Link Prediction classifier using scikit-learn with features derived from the Neo4j Data Science library.

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