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Full Description
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.
Contents
1: Introduction
2: Preliminaries
PART I Vertex-Centric Programming Model
3: Vertex-Centric Message Passing (Pregel-like) Systems
4: Vertex-Centric Message-Passing Systems Beyond Pregel
5: Vertex-Centric Systems with Shared Memory Abstraction
PART II Beyond Vertex-Centric Programming Model
6: Matrix Algebra-Based Systems
7: Subgraph-Centric Programming Models
8: DBMS-Inspired Systems
PART III Miscellaneous Issues
9: More on Single-Machine Systems
10: Hardware-Accelerated Systems
11: Temporal and Streaming Graph Analytics
12: Conclusions and Future Directions
Bibliography
