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Towards efficient simulation-based constrained temporal graph pattern matching

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Abstract

In the context of searching a single data graph G, graph pattern matching is to find all the occurrences of a pattern graph Q in G, specified by a matching rule. It is of paramount importance in many real applications such as social network analysis and cyber security, among others. A wide spectrum of studies target general graph pattern matching. However, to analyze time-relevant services such as studying the spread of diseases and detecting attack patterns, it is attractive to study inexact temporal graph pattern matching. Hence, in this paper, we propose a relaxed matching rule called constrained temporal dual simulation, and study simulation-based constrained temporal graph pattern matching which guarantees that the matching result (i) preserves the ancestor and descendant temporal connectivities; and (ii) implements edge-to-temporal path mapping. We devise a decomposition-based matching method, which first decomposes the data graph into Source Temporal Connected Components, and then performs matching on decomposed subgraphs. To speed up the matching, we define child/parent dependency relation tables and propose an efficient double hierarchical traverse strategy. Considering that the temporal graphs are naturally dynamic, we further propose update algorithms. An extensive empirical study over real-world and synthetic temporal graphs has demonstrated the effectiveness and efficiency of our approach.

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Availability of data and materials

The datasets and materials are available in Github (https://github.com/ZJU-DAILY/CTGPM).

Notes

  1. Highschool is available at http://www.sociopatterns.org/datasets/.

  2. SNAP is available at https://snap.stanford.edu/data/.

  3. JGraphT is available at http://jgrapht.org/.

  4. Code of Timing is available at https://github.com/pkumod/timingsubg.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62302451, the Natural Science Foundation of Zhejiang Province of China under Grant No. LQ22F020018, the Key Research Project of Zhejiang Province of China under Grant No. 2023C01048, and the National Natural Science Foundation of China under Grant numbers 62276233, 62025206.

Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 62302451, the Natural Science Foundation of Zhejiang Province of China under Grant No. LQ22F020018, the Key Research Project of Zhejiang Province of China under Grant No. 2023C01048, and the National Natural Science Foundation of China under Grant numbers 62276233, 62025206.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, 310023, Zhejiang, China

    Tianming Zhang, Bin Cao & Jing Fan

  2. School of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Hangzhou, 310013, Zhejiang, China

    Xinwei Cai, Lu Chen & Yunjun Gao

  3. School of Computer Science and Engineering, University of New South Wales, Sydney, 2052, New South Wales, Australia

    Zhengyi Yang

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  1. Tianming Zhang
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Correspondence to Tianming Zhang or Yunjun Gao.

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Zhang, T., Cai, X., Chen, L. et al. Towards efficient simulation-based constrained temporal graph pattern matching. World Wide Web 27, 22 (2024). https://doi.org/10.1007/s11280-024-01259-2

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