Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Parallel Graph Processing

  • Da YanEmail author
  • Hang Liu
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_272


The term parallel graph processing refers to the use of multiple cores to process a graph for the purpose of (1) speeding up of processing and (2) scaling to bigger graphs. The environment can be (1) a stand-alone machine running multiple threads or (2) a distributed cluster of machines (i.e., the shared-nothing architecture).


Modern big graph processing systems place emphasis on two aspects:
  1. 1.

    user-friendliness: the programming interface should be designed based on an intuitive computation model, to allow algorithm developers to focus on the graph analytics logic without touching low-level execution details (e.g., network communication);

  2. 2.

    efficiency: the underlying execution engine should guarantee high-throughput execution and automatically support fault tolerance and horizontal/vertical scaling.


Since comprehensive surveys of this area already exist (Yan et al. 2017a,d), this chapter aims at a succinct and more up-to-date review of the key programming...

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of Alabama at BirminghamBirminghamUSA
  2. 2.University of Massachusetts LowellLowellUSA