Online Course Data Science with Neo4j Setting Up your Development Environment Exploratory Data Analysis Recommendations Predictions Summary: Data Science with Neo4j Want to Speak? Get $ back. Predictions About this module In this module you will learn how to build… Read more →

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 functions 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 Algorithms library

Exercise 2: Building a binary classifier

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

Exercise 2

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 functions 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 Algorithms library

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