| Description |
1 online resource |
| Series |
SpringerBriefs in computer science, 2191-5768 |
|
SpringerBriefs in computer science.
|
| Contents |
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 |
| Summary |
There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment. This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied to solve graph problems that cannot be efficiently solved in vertex-centric model, and are the promising next-generation models for big graph analytics. For each part, the authors introduce the state-of-the-art systems, emphasizing on both their technical novelties and hands-on experiences of using them. The systems introduced include Giraph, Pregel+, Blogel, GraphLab, CraphChi, X-Stream, Quegel, SystemML, etc. Readers will learn how to design graph algorithms in various graph analytics systems, and how to choose the most appropriate system for a particular application at hand. The target audience for this book include beginners who are interested in using a big graph analytics system, and students, researchers and practitioners who would like to build their own graph analytics systems with new features |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Graph algorithms.
|
|
Graph theory -- Data processing.
|
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MATHEMATICS -- Numerical Analysis.
|
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Graph algorithms
|
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Graph theory -- Data processing
|
| Form |
Electronic book
|
| Author |
Tian, Yuanyuan
|
|
Cheng, James
|
| ISBN |
9783319582177 |
|
3319582178 |
|
