Graph simulation on large scale temporal graphs

  • Yuliang Ma
  • Ye YuanEmail author
  • Meng Liu
  • Guoren Wang
  • Yishu Wang


Graph simulation is one of the most important queries in graph pattern matching, and it is being increasingly used in various applications, e.g., protein interaction networks, software plagiarism detection. Most previous studies mainly focused on the simulation problem on static graphs, which neglected the temporal factors in daily life. In this paper, we investigate a novel problem, namely, temporal bounded simulation on temporal graphs. Specifically, given a pattern query Q with bound constraint and a temporal graph G, we aim to find out the matches Q(G) of Q on G, such that Q(G) satisfies temporal reachability and bound constraint of Q. To tackle this problem efficiently, we propose a simulation matching framework, which consists of three phases, namely pattern segmentation, temporal bounded simulation of pattern segments, and result integration. We first divide the Q into simple small subgraphs (segments). We construct a temporal reachable tree of G. Based on the tree, we propose a basic matching algorithm for the pattern segments. Furthermore, we propose two optimization algorithms by leveraging multi-layer partition strategy to accelerate query processing. After that, we integrate the simulation results obtained from the pattern segments according to the constraints of temporal reachability and the bound of query pattern. Finally, we use real temporal and synthetic datasets to empirically verify the efficiency and effectiveness of our solutions for temporal bounded simulation matching.


Graph simulation Pattern match Temporal graph 



This research is partially funded by the National Key Research and Development Program of China (Grant No. 2016YFC1401900), the National Natural Science Foundation of China (Grant Nos. 61572119, 61572121, 61622202, 61732003, 61729201, 61702086, and U1401256), the Fundamental Research Funds for the Central Universities (Grant Nos. N171604007, and N171904007).

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants.

Human and Animal Rights

This article does not contain any studies involving human participants and/or animals by any of the authors.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yuliang Ma
    • 1
  • Ye Yuan
    • 1
    Email author
  • Meng Liu
    • 1
  • Guoren Wang
    • 2
  • Yishu Wang
    • 1
  1. 1.Northeastern UniversityShenyangChina
  2. 2.Beijing Institute of TechnologyBeijingChina

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