The VLDB Journal

, Volume 28, Issue 6, pp 871–896 | Cite as

Efficient distributed reachability querying of massive temporal graphs

  • Tianming Zhang
  • Yunjun GaoEmail author
  • Lu Chen
  • Wei Guo
  • Shiliang Pu
  • Baihua Zheng
  • Christian S. Jensen
Regular Paper


Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper’s study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed temporal graphs. We also present algorithms that exploit TVL to achieve efficient support for distributed reachability querying over temporal graphs in Pregel-like systems. The algorithms exploit several optimizations that hinge upon non-trivial lemmas. Extensive experiments using massive real and synthetic temporal graphs are conducted to provide detailed insight into the efficiency and scalability of the proposed methods, covering both index construction and query processing. Compared with the state-of-the-art methods, the TVL based query algorithms are capable of up to an order of magnitude speedup with lower index construction overhead.


Graph Reachability Distributed processing Query processing Algorithm 



This work was supported in part by the National Key R&D Program of China under Grant No. 2018YFB1004003, the NSFC under Grant No. 61972338, the NSFC-Zhejiang Joint Fund under Grant No. U1609217, the ZJU-Hikvision Joint Project, and the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. Yunjun Gao is the corresponding author of the work.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tianming Zhang
    • 1
  • Yunjun Gao
    • 1
    • 2
    Email author
  • Lu Chen
    • 3
  • Wei Guo
    • 1
  • Shiliang Pu
    • 4
  • Baihua Zheng
    • 5
  • Christian S. Jensen
    • 3
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouChina
  2. 2.The Key Lab of Big Data Intelligent Computing of Zhejiang ProvinceZhejiang UniversityHangzhouChina
  3. 3.Department of Computer ScienceAalborg UniversityAalborgDenmark
  4. 4.Hangzhou Hikvision Digital Technology Co., Ltd.HangzhouChina
  5. 5.School of Information SystemsSingapore Management UniversitySingaporeSingapore

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