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.
