© 2017

Systems for Big Graph Analytics


Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Da Yan, Yuanyuan Tian, James Cheng
    Pages 1-2
  3. Think Like a Vertex

    1. Front Matter
      Pages 3-3
    2. Da Yan, Yuanyuan Tian, James Cheng
      Pages 5-21
    3. Da Yan, Yuanyuan Tian, James Cheng
      Pages 23-36
    4. Da Yan, Yuanyuan Tian, James Cheng
      Pages 37-50
  4. Think Like a Graph

    1. Front Matter
      Pages 51-51
    2. Da Yan, Yuanyuan Tian, James Cheng
      Pages 53-65
    3. Da Yan, Yuanyuan Tian, James Cheng
      Pages 67-76
  5. Think Like a Matrix

    1. Front Matter
      Pages 77-77
    2. Da Yan, Yuanyuan Tian, James Cheng
      Pages 79-89
    3. Da Yan, Yuanyuan Tian, James Cheng
      Pages 91-92

About this book


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.


Big graph analytics Big data Vertex-centric Graph-centric Matrix Graph System Block-centric Pregel GraphLab Blogel Quegel SystemML

Authors and affiliations

  1. 1.Department of Computer and Information ScienceUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA
  3. 3.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatin, N.T.Hong Kong

Bibliographic information

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