Large-scale graph processing systems: a survey

Ning LIU; Dong-sheng LI; Yi-ming ZHANG; Xiong-lve LI

doi:10.1631/FITEE.1900127

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Large-scale graph processing systems: a survey

Regular Papers | Updated：2022-05-19

- Large-scale graph processing systems: a survey
  Enhanced Publication
- 大规模图计算系统综述
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 3, Pages: 384-404(2020)
- Affiliations：
  
  Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410000, China
- Author bio：
  
  [ "Ning LIU, E-mail: liuning17a@nudt.edu.cn" ]
  [ "", "Dr. Dong-sheng LI, corresponding author of this invited review article, received the BS degree (with honors) and PhD degree (with honors) in computer science from College of Computer Science, National University of Defense Technology (NUDT), Changsha, China, in 1999 and 2005, respectively. He was awarded the prize of National Excellent Doctoral Dissertation by the Ministry of Education of China in 2008. He is now a full professor at the National Lab for Parallel and Distributed Processing, NUDT. He is a corresponding expert of Frontiers of Information Technology & Electronic Engineering. His research interests include parallel and distributed computing, cloud computing, and large-scale data management" ]
  [ "Yi-ming ZHANG, E-mail: zhangyiming@nudt.edu.cn" ]
  [ "Xiong-lve LI, E-mail: lixionglve17@nudt.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1900127
  CLC： TP391.41
- Published：2020-03，
  
  Received：05 March 2019，
  
  Revised：12 November 2019，
- Accepted：
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NING LIU, DONG-SHENG LI, YI-MING ZHANG, et al. Large-scale graph processing systems: a survey. [J]. Frontiers of information technology & electronic engineering, 2020, 21(3): 384-404.
DOI：

NING LIU, DONG-SHENG LI, YI-MING ZHANG, et al. Large-scale graph processing systems: a survey. [J]. Frontiers of information technology & electronic engineering, 2020, 21(3): 384-404. DOI： 10.1631/FITEE.1900127.

摘要

图是描述实体之间关系的一种重要数据结构。现实世界中许多应用领域非常依赖图数据。然而，由于图计算应用与传统应用的显著差异，利用通用平台处理图计算应用是低效的，这极大推动了专用图计算系统的研究。本综述系统地对图算法和图计算应用进行分类，将现有图计算系统划分为通用和专用系统，并详细总结。深入分析图计算系统的实现技术，包括编程模型、分区策略、通信模型、执行模型和容错机制。最后，分析图计算领域最新进展，并提出有待进一步研究的4个问题。

Abstract

Graph is a significant data structure that describes the relationship between entries. Many application domains in the real world are heavily dependent on graph data. However

graph applications are vastly different from traditional applications. It is inefficient to use general-purpose platforms for graph applications

thus contributing to the research of specific graph processing platforms. In this survey

we systematically categorize the graph workloads and applications

and provide a detailed review of existing graph processing platforms by dividing them into general-purpose and specialized systems. We thoroughly analyze the implementation technologies including programming models

partitioning strategies

communication models

execution models

and fault tolerance strategies. Finally

we analyze recent advances and present four open problems for future research.

关键词

图算法图计算应用图计算系统

Keywords

Graph workloadsGraph applicationsGraph processing systems

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