Introduction to Graph Computing

Introduction

• Graph databases and libraries

Understanding Graph Data

• The graph as a data structure
• Using vertices (dots) and edges (lines) to model real-world scenarios

Using Graph Databases to Model, Persist and Process Graph Data

• Local graph algorithms/traversals
• neo4j, OrientDB and Titan

Exercise: Modeling Graph Data with neo4j

• Whiteboard data modeling

Beyond Graph Databases: Graph Computing

• Understanding the property graph
• Graph modeling different scenarios (software graph, discussion graph, concept graph)

Solving Real-World Problems with Traversals

• Algorithmic/directed walk over the graph
• Determining circular cependencies

Case Study: Ranking Discussion Contributors

• Ranking by number and depth of contributed discussions
• A note on sentiment and concept analysis

Graph Computing: Local, In-Memory Graph toolkits

• Graph analysis and visualization
• JUNG, NetworkX, and iGraph

Exercise: Modeling Graph Data with NetworkX

• Using NetworkX to model a complex system

Graph Computing: Batch Processing Graph Frameworks

• Leveraging Hadoop for storage (HDFS) and processing (MapReduce)
• Overview of iterative algorithms
• Hama, Giraph, and GraphLab

Graph Computing: Graph-Parallel Computation

• Unifying ETL, exploratory analysis, and iterative graph computation within a single system
• GraphX

Setup and Installation

• Hadoop and Spark

GraphX Operators

• Property, structural, join, neighborhood aggregation, caching and uncaching

Iterating with Pregel API

• Passing arguments for sending, receiving and computing

Building a Graph

• Using vertices and edges in an RDD or on disk

Designing Scalable Algorithms

• GraphX Optimization

Accessing Additional Algorithms

• PageRank, Connected Components, Triangle Counting

Exercis: Page Rank and Top Users

• Building and processing graph data using text files as input

Deploying to Production

Closing Remarks