From Graph to Knowledge Graph – Algorithms and Applications

Learn the fundamental algorithms and theories used for understanding large-scale graphs and knowledge graphs.

About this course

Many real-world 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’lll 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 1: Introduction and Overview
Module 2: Graph Properties and Applications
Module 3: Graph Representation Learning
Module 4: Knowledge Graph Fundamentals and Construction
Module 5: Knowledge Graph Inference and Applications

Prerequisites

Advanced math skills
Basic programming skills
Fundamental knowledge on machine learning and deep learning techniques
Skills equivalent to the following course on big data analysis
- DAT223.1x:Processing Big Data with Azure Data Lake Analytics

Course Staff

Yuxiao Dong

Senior Applied Scientist

Microsoft

Yuxiao Dong is an Senior Applied Scientist at Microsoft Research, Redmond. His research focuses on data mining, network science, and computational social science, with an emphasis on applying computational models to address problems in large-scale networked systems, such as the Microsoft Academic Graph (MAG). His research has won four best paper awards/nominations as well as the 2017 ACM SIGKDD Doctoral Dissertation Award Honorable Mention.

Iris Shen

Principal Data Scientist

Microsoft

Iris Shen is a Principal Data Scientist at Microsoft Research and holds a Ph.D. in Operations Research from University of Southern California. She is the lead data scientist for Microsoft Academic which leverages the cognitive power of machine learning and the Microsoft Academic Graph to assist humans in scientific research.  Her past work includes business intelligence solutions for various web services and large-scale optimization applications in the supply chain management domain.