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Graph-Powered Machine Learning First Steps

From graph analytics to graph neural networks: Making the most of your graph data

Topic: Data
Jörg Schad

Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.

Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand:

  • Why graphs are such a powerful abstraction
  • How to leverage knowledge graphs
  • The graph ecosystem including its many many powerful open source tools
  • How to extract value from graphs using graph analytics and graph algorithms
  • How to combine deep learning and graphs
  • How we can learn graphs features using graph neural networks

And you’ll be able to:

  • Identify suitable graph use cases
  • Use open source tools to create, manipulate, and query graphs
  • Leverage graph algorithms to analyze graphs
  • Apply machine learning techniques on graphs
  • Utilize the latest graph neural network techniques

This training course is for you because...

  • You’re a data scientist who wants to extend your toolset.
  • You’re a technical leader or architect who wants to understand the power of graphs and machine learning.
  • You’re an engineer who wants to learn about graphs and machine learning.

Prerequisites

  • Familiarity with machine learning concepts and tools (e.g., TensorFlow) and Python (useful but not required)

Recommended preparation:

Recommended follow-up:

About your instructor

  • Jörg Schad is Head of Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He’s a frequent speaker at meetups, international conferences, and lecture halls.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Course overview (15 minutes)

  • Presentation: Jupyter Notebook and infrastructure overview

Graph basics and queries (40 minutes)

  • Presentation: What are graphs, and why are they so powerful?; graph properties and characteristics; graph storage and queries
  • Hands-on exercises: Work with SPARQL; work with property graphs
  • Q&A
  • Break (5 minutes)

Graph analytics and algorithms (40 minutes)

  • Presentation: Graph analytics and algorithms; identifying graph problems; complex graphs use cases
  • Hands-on exercises: Use graph algorithms for reachability and shortest path queries; implement basic graph analytics—collaborative filtering; use PageRank; explore graph-based fraud detection
  • Q&A
  • Break (5 minutes)

Graph toolset (40 minutes)

  • Presentation: Open source graph tools; graph embeddings for machine learning
  • Hands-on exercise: Use NetworkX; use the Deep Graph Library; generate graph embeddings with node2vec
  • Q&A
  • Break (5 minutes)

Graph neural network (40 minutes)

  • Presentation: Introduction to graph neural networks; advanced graph neural networks
  • Hands-on exercises: Build a graph convolutional network from scratch; use the Deep Graph Library and advanced graph convolutional networks
  • Q&A
  • Break (5 minutes)

End-to-end project (40 minutes)

  • Presentation: The machine learning cycle; next steps
  • Hands-on exercises: Explore citation graph data and predict data quality, from data preparation, model building, evaluation, and retraining to inference

Wrap-up and Q&A (5 minutes)