1 Introduction; Reference; Part I Think Like a Vertex; 2 Pregel-Like Systems; 2.1 Google's Pregel; 2.1.1 Computation Model; 2.1.2 Algorithm Design; 2.2 Pregel-Like Systems; 2.2.1 Communication Mechanism; 2.2.2 Load Balancing; 2.2.3 Out-of-Core Execution; 2.2.4 Fault Recovery; 2.2.5 On-Demand Querying; References; 3 Hands-On Experiences ; 3.1 Why Beginning with BigGraph@CUHK; 3.2 System Deployment and Running; 3.3 System Design and Basic Pregel API; 3.3.1 The ``utils'' Library; 3.3.2 The ``basic'' Library; 3.3.3 Summary; References; 4 Shared Memory Abstraction

4.1 Programming Interface and Its Expressiveness4.2 GraphLab and PowerGraph; 4.2.1 GraphLab; 4.2.2 PowerGraph; 4.2.3 Maiter: An Accurate Asynchronous Model; 4.3 Single-PC Disk-Based Systems; 4.3.1 GraphChi; 4.3.2 X-Stream; 4.3.3 VENUS; 4.3.4 GridGraph; References; Part II Think Like a Graph; 5 Block-Centric Computation; 5.1 Comparison: Block-Centric vs. Vertex-Centric; 5.2 The Blogel System; 5.3 Get Started with Blogel; 5.3.1 Blogel Graph Partitioners; 5.3.2 Block-Centric API; References; 6 Subgraph-Centric Graph Mining; 6.1 Problem Definition and Existing Methods; 6.2 The G-Thinker System