About this course: Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.
Created by: University of California, San Diego
Taught by: Amarnath Gupta, Director, Advanced Query Processing Lab
San Diego Supercomputer Center (SDSC)
| Basic Info | Course 5 of 6 in the Big Data Specialization |
| Commitment | 4 Weeks, 3-5 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
WEEK 2
Introduction to Graphs
Welcome! This week we will get a first exposure to graphs and their use in everyday life. By the end of the module you will be able to create a graph applying core mathematical properties of graphs, and identify the kinds of analysis questions one might be able to ask of such a graph. We hope the you will be inspired as to how graphical representations might enable you to answer new Big Data problems!
8 videos, 2 readings
- Reading: What to learn in this module
- Video: What is a Graph?
- Video: Why Graphs?
- Discussion Prompt: Let's Discuss: What else do you interact with that can be represented as a graph?
- Video: Why Graphs? Example 1: Social Networking
- Video: Why Graphs? Example 2: Biological Networks
- Video: Why Graphs? Example 3: Human Information Network Analytics
- Video: Why Graphs? Example 4: Smart Cities
- Video: The Purpose of Analytics
- Video: What are the impact of Big Data's V's on Graphs?
- Discussion Prompt: Optional: What's the most interesting graph you reviewed?
- Reading: Download Slides for this Module
Graded: Introduction to Graphs
Graded: Graphs in Everyday Life
WEEK 3
Graph Analytics
17 videos, 3 readings
- Reading: What to learn in this module
- Video: Focusing On Graph Analytics Techniques
- Reading: If this module takes a little longer... that's OK!
- Reading: Download All Slides for Module 3
- Video: Path Analytics
- Video: The Basic Path Analytics Question: What is the Best Path?
- Video: Applying Dijkstra's Algorithm
- Video: Inclusion and Exclusion Constraints
- Discussion Prompt: Let's Discuss: Where do you see path problems in your life?
- Video: Connectivity Analytics
- Video: Disconnecting a Graph
- Video: Connectedness: Indegree and Outdegree
- Video: Community Analytics and Local Properties
- Discussion Prompt: Let's Discuss: What kind of community analytics question would you like to ask?
- Video: Global Property: Modularity
- Video: Centrality Analytics
- Video: Optional Lecture 1: Bi-directional Dijkstra Algorithm
- Video: Optional Lecture 2: Goal-directed Dijkstra Algorithm
- Video: Optional Lecture 3: Power Law Graphs
- Video: Optional Lecture 4: Measuring Graph Evolution
- Video: Optional Lecture 5: Eigenvector Centrality
- Video: Optional Lecture 6: Key Player Problems
Graded: Graph Analytics Applications
Graded: Connectivity, Community, and Centrality Analytics
WEEK 4
Graph Analytics Techniques
Welcome to the 4th module in the Graph Analytics course. Last week, we got a glimpse of a number of graph properties and why they are important. This week we will use those properties for analyzing graphs using a free and powerful graph analytics tool called Neo4j. We will demonstrate how to use Cypher, the query language of Neo4j, to perform a wide range of analyses on a variety of graph networks.
10 videos, 12 readings
- Video: Welcome to Graph Analytics Techniques
- Reading: About the Supplementary Resources
- Reading: Downloading, Installing, and Running Neo4j - Supplementary Resources
- Video: Hands-On: Downloading, Installing, and Running Neo4j
- Reading: Getting Started With Neo4j - Supplementary Resources
- Video: Hands-On: Getting Started With Neo4j
- Reading: Adding to and Modifying a Graph - Supplementary Resources
- Video: Hands-On: Modifying a Graph With Neo4j
- Reading: Download datasets used in this Graph Analytics with Neo4j
- Reading: Importing Data Into Neo4j - Supplementary Resources
- Video: Hands-On: Importing Data Into Neo4j
- Reading: FAQ
- Reading: Basic Queries in Neo4j With Cypher - Supplementary Resources
- Video: Hands-On: Basic Queries in Neo4j With Cypher - Part 1
- Video: Hands-On: Basic Queries in Neo4j With Cypher - Part 2
- Reading: Path Analytics in Neo4j With Cypher - Supplementary Resources
- Video: Hands-On: Path Analytics in Neo4j Using Cypher - Part 1
- Video: Hands-On: Path Analytics in Neo4j Using Cypher - Part 2
- Reading: Connectivity Analytics in Neo4j with Cypher - Supplementary Resources
- Video: Hands-On: Connectivity Analytics in Neo4j With Cypher
- Reading: Assignment: Practicing Graph Analytics in Neo4j With Cypher
- Reading: Download All Neo4j Supplementary Resources (PDFs)
Graded: Quiz: Graph Analytics With Neo4j
Graded: Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'
WEEK 5
Computing Platforms for Graph Analytics
In the last two modules we have learned about graph analytics and graph data management. This week we will study how they come together. There are programming models and software frameworks created specifically for graph analytics. In this module we'll give an introductory tour of these models and frameworks. We will learn to implement what you learned in Week 2 and build on it using GraphX and Giraph.
11 videos, 7 readings
- Video: Introduction: Large Scale Graph Processing
- Video: A Parallel Programming Model for Graphs
- Video: Pregel: The System That Changed Graph Processing
- Video: Giraph and GraphX
- Video: Beyond Single Vertex Computation
- Video: Introduction to GraphX: Hands-On Demonstrations
- Reading: Datasets and Libraries for Example of Analytics Hands On
- Reading: Download all of the readings for this section as a PDF
- Video: Hands On: Building a Graph
- Reading: Hands On: Building a Graph Reading
- Video: Hands On: Building a Degree Histogram
- Reading: Hands On: Building a Degree Histogram Reading
- Video: Hands On: Plot the Degree Histogram
- Reading: Hands On: Plot the Degree Histogram Reading
- Video: Hands On: Network Connectedness and Clustering Components
- Reading: Hands On: Network Connectedness and Clustering Components Reading
- Video: Hands On: Joining Graph Datasets
- Reading: Hands On: Joining Graph Datasets Reading
Graded: Using GraphX
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| Access to course materials | Available |
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Ratings and Reviews
This module introduced a lot of technical knowledge and theory around Graph Analytics. I understand there is great value in Graph analysis but I got a bit confused with so many examples, programming languages and many technical terms. I think Graph Analysis should be a specialisation on its own.
It was the most difficult course
Extremely complex, and the material to understand was excellent!
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Dd
Really enjoyed the Neo4j module. I would have liked to explore and have feedback for analysis that I am interested in exploring. It would be great to have direct contact with instructors. The last module was a lot of copying and pasting code that I feel will be extremely difficult to retain. Not sure what the purpose was.