Titlebook: Systems for Big Graph Analytics; Da Yan,Yuanyuan Tian,James Cheng Book 2017 The Author(s) 2017 Big graph analytics.Big data.Vertex-centric

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Hands-On Experiencesable for educational purposes due to its neat design. Readers only interested in surveying existing big graph analytics systems may safely skip this chapter, while researchers and practitioners who are interested in using Pregel-like system and building their own Big Data systems could benefit from reading this chapter.

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Shared Memory Abstractiont although the programming interface of these systems simulates a shared memory environment, the underlying execution engine is not shared memory. We focus on introducing the various models implementing shared memory abstraction, and leave readers to explore the system usage by following their respective system websites.

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Introduction, have witnessed a surging interest in developing big graph analytics systems. Tens of systems have been developed for processing big graphs. Although they enrich the tools available for users to analyze big graphs, it is difficult for beginners of this field to gather up the threads of various syste

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Block-Centric Computationwith a large diameter. This chapter describes a novel block-centric computation model that overcomes the weaknesses of vertex-centric computation, and that significantly speeds up iterative graph computation. We also include a hands-on tutorial on how to get started with Blogel, a state-of-the-art b

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Subgraph-Centric Graph Miningng, rendering the distributed execution communication-intensive. However, graph mining tasks are often computation-intensive, and cannot be efficiently executed with a data-intensive system. The vertex-centric API is also unsuitable for writing a graph mining algorithm that often checks subgraphs ra

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