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Graph simulation on large scale temporal graphs

  • Yuliang Ma
  • Ye YuanEmail author
  • Meng Liu
  • Guoren Wang
  • Yishu Wang
Article
  • 22 Downloads

Abstract

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.

Keywords

Graph simulation Pattern match Temporal graph 

Notes

Acknowledgments

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.

References

  1. 1.
    Aggarwal CC, Wang H, et al. (2010) Managing and mining graph data, vol 40. SpringerGoogle Scholar
  2. 2.
    de Andrade HS, Sales CL (2009) Pattern match query in a large graph database. Encontros Universitários da UFC 2(1):1544Google Scholar
  3. 3.
    Cacciari L, Rafiq O (1995) A temporal reachability analysis. In: International conference on protocol specification, testing and verification. Springer, pp 35–49Google Scholar
  4. 4.
    Chan J, Bailey J, Leckie C (2008) Discovering correlated spatio-temporal changes in evolving graphs. Knowl Inf Syst 16(1):53–96CrossRefGoogle Scholar
  5. 5.
    Chen L, Cong G, Cao X, Tan KL (2015) Temporal spatial-keyword top-k publish/subscribe. In: 2015 IEEE 31St international conference on data engineering. IEEE, pp 255–266Google Scholar
  6. 6.
    Fan W, Li J, Ma S, Tang N, Wu Y, Wu Y (2010) Graph pattern matching: from intractable to polynomial time. Proc VLDB Endow 3(1–2):264–275CrossRefGoogle Scholar
  7. 7.
    Fan W, Li J, Wang X, Wu Y (2012) Query preserving graph compression. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data. ACM, pp 157–168Google Scholar
  8. 8.
    Fan W, Wang X, Wu Y (2014) Querying big graphs within bounded resources. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 301– 312Google Scholar
  9. 9.
    Fan W, Wang X, Wu Y, Deng D (2014) Distributed graph simulation: impossibility and possibility. Proc VLDB Endow 7(12):1083–1094CrossRefGoogle Scholar
  10. 10.
    He H, Singh AK (2008) Graphs-at-a-time: query language and access methods for graph databases. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, pp 405–418Google Scholar
  11. 11.
    Henzinger MR, Henzinger TA, Kopke PW (1995) Computing simulations on finite and infinite graphs. In: Proceedings of IEEE 36th annual foundations of computer science. IEEE, pp 453–462Google Scholar
  12. 12.
    Holme P, Saramäki J (2013) Temporal networks. SpringerGoogle Scholar
  13. 13.
    Huang S, Fu AWC, Liu R (2015) Minimum spanning trees in temporal graphs. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data. ACM, pp 419–430Google Scholar
  14. 14.
    Jamil H (2011) Computing subgraph isomorphic queries using structural unification and minimum graph structures. In: Proceedings of the 2011 ACM symposium on applied computing. ACM, pp 1053– 1058Google Scholar
  15. 15.
    Kan A, Chan J, Bailey J, Leckie C (2009) A query based approach for mining evolving graphs. In: Proceedings of the Eighth Australasian data mining conference, vol 101. Australian Computer Society, Inc, pp 139–150Google Scholar
  16. 16.
    Kann V (1992) On the approximability of the maximum common subgraph problem. In: Annual symposium on theoretical aspects of computer science. Springer, pp 375–388Google Scholar
  17. 17.
    Kostakos V (2009) Temporal graphs. Physica A: Stat. Mech. Appl. 388 (6):1007–1023CrossRefGoogle Scholar
  18. 18.
    Li X, Cheng Y, Cong G, Chen L (2017) Discovering pollution sources and propagation patterns in urban area. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1863–1872Google Scholar
  19. 19.
    Liu A, Wang W, Shang S, Li Q, Zhang X (2018) Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica 22 (2):335–362CrossRefGoogle Scholar
  20. 20.
    Ma S, Cao Y, Fan W, Huai J, Wo T (2011) Capturing topology in graph pattern matching. Proc VLDB Endow 5(4):310–321CrossRefGoogle Scholar
  21. 21.
    Ma S, Cao Y, Fan W, Huai J, Wo T (2014) Strong simulation: capturing topology in graph pattern matching. ACM Trans Datab Syst (TODS) 39(1):4Google Scholar
  22. 22.
    Ma Y, Yuan Y, Wang G, Bi X, Qin H (2018) Trust-aware personalized route query using extreme learning machine in location-based social networks. Cogn Comput 10(6):965–979CrossRefGoogle Scholar
  23. 23.
    Ma Y, Yuan Y, Wang G, Wang Y, Ma D, Cui P (2019) Local experts finding across multiple social networks. In: International conference on database systems for advanced applications. Springer, pp 536–554Google Scholar
  24. 24.
    Nicosia V, Tang J, Mascolo C, Musolesi M, Russo G, Latora V (2013) Graph metrics for temporal networks. In: Temporal networks. Springer, pp 15–40Google Scholar
  25. 25.
    Pan RK, Saramäki J (2011) Path lengths, correlations, and centrality in temporal networks. Phys Rev E 84(1):016105CrossRefGoogle Scholar
  26. 26.
    Santoro N, Quattrociocchi W, Flocchini P, Casteigts A, Amblard F (2011) Time-varying graphs and social network analysis: temporal indicators and metrics. arXiv:https://arxiv.org/abs/1102.0629
  27. 27.
    Shang S, Chen L, Jensen CS, Wen JR, Kalnis P (2017) Searching trajectories by regions of interest. IEEE Trans Knowl Data Eng 29(7):1549–1562CrossRefGoogle Scholar
  28. 28.
    Shang S, Chen L, Wei Z, Jensen CS, Wen JR, Kalnis P (2015) Collective travel planning in spatial networks. IEEE Trans Knowl Data Eng 28(5):1132–1146CrossRefGoogle Scholar
  29. 29.
    Shang S, Chen L, Wei Z, Jensen CS, Zheng K, Kalnis P (2017) Trajectory similarity join in spatial networks. Proc VLDB Endow 10(11):1178–1189CrossRefGoogle Scholar
  30. 30.
    Shang S, Chen L, Wei Z, Jensen CS, Zheng K, Kalnis P (2018) Parallel trajectory similarity joins in spatial networks. Int J Very Large Data Bases 27 (3):395–420CrossRefGoogle Scholar
  31. 31.
    Shang S, Chen L, Zheng K, Jensen CS, Wei Z, Kalnis P (2018) Parallel trajectory-to-location join. IEEE Trans Knowl Data Eng 31(6):1194–1207CrossRefGoogle Scholar
  32. 32.
    Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P (2012) User oriented trajectory search for trip recommendation. In: Proceedings of the 15th international conference on extending database technology. ACM, pp 156–167Google Scholar
  33. 33.
    Shang S, Ding R, Zheng K, Jensen CS, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. Int J Very Large Data Bases 23 (3):449–468CrossRefGoogle Scholar
  34. 34.
    Tang J, Musolesi M, Mascolo C, Latora V (2009) Temporal distance metrics for social network analysis. In: Proceedings of the 2nd ACM workshop on online social networks. ACM, pp 31–36Google Scholar
  35. 35.
    Ullmann JR (1976) An algorithm for subgraph isomorphism. J ACM (JACM) 23(1):31–42CrossRefGoogle Scholar
  36. 36.
    Wang X, Chai L, Xu Q, Yang Y, Li J, Wang J, Chai Y (2019) Efficient subgraph matching on large rdf graphs using mapreduce. Data Sci Eng 4(1):24–43CrossRefGoogle Scholar
  37. 37.
    Whitbeck J, Dias de Amorim M, Conan V, Guillaume JL (2012) Temporal reachability graphs. In: Proceedings of the 18th annual international conference on mobile computing and networking. ACM, pp 377–388Google Scholar
  38. 38.
    Wipke WT, Rogers D (1984) Rapid subgraph search using parallelism. J Chem Inf Comput Sci 24(4):255–262CrossRefGoogle Scholar
  39. 39.
    Wu H, Cheng J, Huang S, Ke Y, Lu Y, Xu Y (2014) Path problems in temporal graphs. Proc VLDB Endow 7(9):721–732CrossRefGoogle Scholar
  40. 40.
    Wu H, Huang Y, Cheng J, Li J, Ke Y (2016) Efficient processing of reachability and time-based path queries in a temporal graph. arXiv:https://arxiv.org/abs/1601.05909
  41. 41.
    Xu Y, Huang J, Liu A, Li Z, Yin H, Zhao L (2017) Time-constrained graph pattern matching in a large temporal graph. In: Asia-Pacific Web (APWeb) and web-age information management (WAIM) joint conference on web and big data. Springer, pp 100–115Google Scholar
  42. 42.
    Yuan Y, Lian X, Chen L, Yu JX, Wang G, Sun Y (2017) Keyword search over distributed graphs with compressed signature. IEEE Trans Knowl Data Eng 29(6):1212–1225CrossRefGoogle Scholar
  43. 43.
    Yue X, Xi M, Chen B, Gao M, He Y, Xu J (2019) A revocable group signatures scheme to provide privacy-preserving authentications. Mobile Networks and ApplicationsGoogle Scholar
  44. 44.
    Zhang S, Yang J, Jin W (2010) Sapper: subgraph indexing and approximate matching in large graphs. Proc VLDB Endow 3(1–2):1185–1194CrossRefGoogle Scholar
  45. 45.
    Zhao K, Chen L, Cong G (2016) Topic exploration in spatio-temporal document collections. In: Proceedings of the 2016 international conference on management of data. ACM, pp 985–998Google Scholar
  46. 46.
    Zhao P, Han J (2010) On graph query optimization in large networks. Proc VLDB Endow 3(1–2):340–351CrossRefGoogle Scholar
  47. 47.
    Zou L, Chen L, Özsu MT (2009) Distance-join: pattern match query in a large graph database. Proc VLDB Endow 2(1):886–897CrossRefGoogle Scholar

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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