Find Hidden Connections with Graphs you own this product

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
intermediate Neo4j (Graph Data Science library) • intermediate graph theory (weakly connected components) • build a node2vec model with Neo4j
John Maiden
1 week · 6-8 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series ML for Knowledge Graphs with Neo4j liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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You’ve set your sights on a collection of prime off-the-market New York City properties whose value is likely to double in the next couple of years. With your excellent sales skills, you’re confident you can get the owners to sell at a fair price, if you can determine who the owners are. You have a knowledge graph—built from tax records, property deeds, and permits—that identifies entities associated with the properties. These entities might not be the true owners, but identifying them could help you determine who the true owners are.

Your task is to transform the knowledge graph into a representation that can be processed by a machine learning model later. Using simple graph and NLP techniques, including node2vec—one of the most influential algorithms in the graph community—you’ll improve the quality of the data by removing the noise from the knowledge graph. You’ll convert the nodes in your graph into embeddings, analyze how well your embeddings represent the underlying knowledge graph, and develop insights on tuning the node2vec hyperparameters. When you’re done, you’ll have learned techniques for analyzing and visualizing embeddings and associating them to the original graph, helping you determine who the “hidden” owners are.

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
  • 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 techniques for analyzing and visualizing graph embeddings and associating them back to the original graph, and you’ll understand how different schemas can impact the knowledge representation.

  • Leverage a graph method (weakly connected components) and NLP to clean a noisy graph
  • Apply the node2vec model to a graph
  • Analyze a graph embedding space using the k-nearest neighbors algorithm
  • Analyze graph data using Graph Data Science (GDS) libraries
  • View graph embeddings in a 2d space using the t-SNE method


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.