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This project is part of the liveProject series ML for Knowledge Graphs with Neo4j
intermediate Python (pandas, sci-kit learn) • intermediate Neo4j (Cypher, Neo4j Desktop) • intermediate machine learning (embeddings, k-nearest neighbors)
skills learned
build transductive graph models (TransE, TransR) with pyKEEN • build an inductive graph model (GraphSAGE) with Stellargraph
John Maiden
1 week · 6-8 hours per week · INTERMEDIATE

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Look inside

Create a powerful recommendation engine built from an ensemble of graph-based models that will help you tap into New York City’s real estate market by identifying groups of similar properties. You’ll start by working with transductive graph models (TransE and TransR) that are created specifically for knowledge graphs. Transductive learning takes observations from a specific set of training data and applies it to a specific set of test data. Next you’ll build an inductive model (GraphSAGE), which allows for generalized learning on new data (i.e. predictive modeling on previously unseen properties). Lastly, you’ll build the recommender system by using the k-Nearest Neighbor (kNN) algorithm to identify similar properties. When you’re done, you’ll have hands-on experience applying machine learning techniques to real-world knowledge graphs… and possibly a lucrative side hustle.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

John Maiden

John Maiden is a software engineer with a focus on building recommendation systems in the social media space. He’s given presentations about his work at Data Council and ML Conf, and he’s talked about building knowledge graphs on the Data Engineering Podcast. John has built knowledge graphs for real estate at a startup and has worked at JP Morgan Chase, where he led a team that produced personalized insights that were delivered to millions of Chase customers. He has a BA from Hamilton College and a PhD in Physics from University of Wisconsin–Madison.


This liveProject is for data scientists who have a background in graph theory and machine learning and are interested in applying these techniques to a knowledge graph. To begin these liveProjects you’ll need to be familiar with the following:

  • Intermediate Python 3.x skills
  • Ability to create lambda functions and list comprehensions
  • Intermediate Jupyter Notebook skills
  • Ability to execute and debug cells
  • Experience in visualizing pandas output
  • Intermediate Neo4j skills
  • Familiarity with the Cypher query language
  • Intermediate pandas skills
  • Ability to read, write, and query data from csv files
  • Basic graph theory
  • Familiar with the concept of nodes and edges
  • Intermediate machine learning
  • Familiar with the concept of embeddings and the k-nearest neighbor algorithm

you will learn

In this liveProject, you’ll learn to apply machine learning techniques to a real-world knowledge graph.

  • Run inductive models (TransE, TransR) on a graph using pyKEEN
  • Run a transductive model (GraphSAGE) on a graph using Stellargraph
  • Create a recommendation system by combining a graph model with k-nearest neighbors


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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