Yuanyuan Tian

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.

The growing need to deal with massive graphs in real-life applications has led to a surge in the development of big graph analytics platforms. Tens of such big graph systems have already been developed, and more are expected to emerge in the near future. Although several experimental studies have been conducted in recent years that compare the performance of several big graph systems, Big Graph Analytics Platforms is the first text to provide a comprehensive survey that clearly summarizes the key features and techniques developed in existing systems. It aims to help readers get a systematic picture of the landscape of recent big graph systems, focusing not just on the systems themselves, but also on the key innovations and design philosophies underlying them. In addition to the popular vertex-centric systems which espouse a think-like-a-vertex paradigm for developing parallel graph applications, Big Graph Analytics Platforms also covers other programming and computation models, contrasts those against each other, and provides a vision for future research in the field.