Description

A powerful application of graph data science is graph completion, using supervised machine learning. The structure of your connected data serves as your labeled data, and your model is trained to fill in the blanks: missing node labels and missing relationships. 

One common use case for graph completion is entity resolution (ER). ER allows businesses to consolidate customer profiles across multiple data streams to create unique, valuable entity profiles.

Graph-based approaches to entity resolution allow you to use not only the traditional identifiers of an entity – such as names, addresses, and other personal identifiable information – but also actions and behavior to literally “connect the dots” between entities.

In this demo, I’ll show how to use  Neo4j’s Graph Data Science (GDS) library to rapidly develop supervised ML pipelines. We’ll use  ER as a case study and walk through an example to demonstrate how it could be applied to your own data:

  • Quick overview of Entity Resolution (ER) and ER in Graph
  • Dive into an example based on real-world data where we will use GDS Link Prediction Pipelines to train an entity linkage model and predict new entity links in the graph
  • Go over quick procedures for generating resolved entity
  • Query resolved entities out of the graph

Do you want to replicate the demo yourself? I have posted the source code on Github. https://github.com/zach-blumenfeld/demo-lp-for-entity-resolution 


Instructor's Bio

 Zach Blumenfeld
Data Science Product Specialist, Neo4j


Zach Blumenfeld is a graph enthusiast who helps data scientists, engineers, and business leaders understand and implement Graph Analytics to solve challenging business problems.
He has first hand experience with a wide range of modern day analytical challenges, including criminal fraud detection, identity resolution, and recommendation systems. Serving in both data science and software developer capacities, Zach has applied graph computing for law enforcement and government entities in support of missions that counter drug trafficking, human smuggling, money laundering, and child exploitation. He has led the development and deployment of full stack graph systems designed to facilitate broad search and analytical query requirements.
Zach is excited to have recently joined Neo4j as Data Science Product Specialist, where he will help empower the field with Neo4j’s industry leading Graph Data Science (GDS) capabilities.

Webinar

  • 1

    ON-DEMAND WEBINAR: Deep Dive: Rapidly Develop ML Pipelines for Graph Completion with Neo4j

    • Ai+ Training

    • Webinar recording

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