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Machine Learning

Graph-Powered Machine Learning First Steps

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

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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 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:

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)

Your Instructor

  • Jörg Schad

    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.

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