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DAT278x

From Graph to Knowledge Graph – Algorithms and Applications

Instructor: Yuxiao Dong, Iris Shen

Microsoft Research - Microsoft Academic Graph team

First Released: January 2019

Total Expected Learning Time: 15-20 hours

Quick Notes:

Below table highlights the compare the overlap and differences between the KDD 2019 Hands-on Tutorial and the DAT278x edX online course.

DAT278x edX online course KDD 2019 Hands-on Tutorial Notes
Expected learning hours 15-20 hours 6 hours self-paced
Content organization Focus more on general thoeries and detailed algorithms with toy size dataset Focus more on high level introduction on thoeries, skipping alogirithm details, and more in-depth dive to MAG construction and real-life application use-cases.
Dataset / Schema small, simplified MAG schema medium and large, full MAG schema
Environment / Programming Language Azure ADLA / USQL Azure Databricks / PySpark (Python)

About this course

Many real-wold datasets come in the form of graphs. These datasets include social networks, biological networks, knowledge graphs, the World Wide Web, and many more. Having a comprehensive understanding of these networks is essential to truly understand many important applications.

This course introduces the fundamental concepts and tools used in modeling large-scale graphs and knowledge graphs. You will learn a spectrum of techniques used to build applications that use graphs and knowledge graphs. These techniques range from traditional data analysis and mining methods to the emerging deep learning and embedding approaches.

What you'll learn

  • Explore large-scale networks with different structures and properties;
  • Learn graph representations using advanced deep learning and embedding techniques;
  • Utilize NLP fundamentals to build knowledge graphs;
  • Use knowledge graphs in modern search applications;
  • Model knowledge graphs using embedding methods.

Course Syllabus

Module No. Title Slides Lab (Using USQL)
I Introduction and Overview link Setup; Smoke Test Script; Data: 1.PaperAuthor, 2.PaperVenue;
II Graph Properties and Applications link n/a
III Graph Representation Learning link Instructions; Script; same data as above
IV Knowledge Graph Fundamentals and Construction link n/a
V Knoledge Graph Inference and Applications link Instructions; Scripts; same data as above