Skip to main content
SpringerLink
Log in

Evolution pattern mining on dynamic social network

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Recently, due to the popularity of social websites and apps, considerable attention has been paid to the analysis of the structure of social networks. Clearly, social networks usually evolve over time; some new users and relationships are established; and some obsolete ones are removed. This dynamic feature definitely increases the complexity of pattern discovery. In this paper, we introduce a new representation to express the dynamic social network and a new type of pattern, the evolution pattern, to capture the interaction evolutions in a dynamic social network. Furthermore, a novel algorithm, evolution pattern miner (EPMiner), is developed to efficiently discover the evolution characteristics. EPMiner also employs some pruning strategies to effectively reduce the search space to improve the performance. The experimental results on several datasets show the efficiency and the scalability of EPMiner for extracting interaction evolution in dynamic networks. Finally, we apply EPMiner on real datasets to show the practicability of evolution pattern mining.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Allen J (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843

    Article  Google Scholar 

  2. Asur S, Parthasarathy S, Ucar D (2007) An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’07), 913–921

  3. Azaouzi M, Rhouma D, Romdhane L (2019) Community detection in large-scale social networks: state-of-the-art and future directions. Soc Netw Anal Min 9:23

    Article  Google Scholar 

  4. Berlingerio M, Bonchi F, Bringmann B, Gionis A (2009) Mining Graph Evolution Rules. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD’09), 115–130

  5. Borgwardt K, Kriegel H, Wackersreuther P (2006) Pattern mining in frequent dynamic subgraphs. In Proceedings of the 6th International Conference on Data Mining (ICDM’06), 818–822

  6. Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06), 554–560

  7. Chen Y, Jiang J, Peng W, Lee S (2010) An efficient algorithm for mining time interval-based patterns in large databases. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10), 49–58

  8. Chen Y, Peng W, Lee S (2015) Mining temporal patterns in time interval-based data. IEEE Trans Knowl Data Eng 27(12):3318–3331

    Article  Google Scholar 

  9. Chi Y, Song X, Zhou D, Hino K, Tseng B (2007) Evolutionary Spectral Clustering by Incorporating Temporal Smoothness. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07), 153–162

  10. Desikan P, Srivastava J (2004) Mining Temporally Evolving Graphs. In Proceedings of the 6th International Workshop on Knowledge Discovery from the Web (WEBKDD’04)

  11. Inokuchi A, Washio T (2008) A fast method to mine frequent subsequences from graph sequence data. In Proceeding of the 8th IEEE International Conference on Data Mining (ICDM’08), 303–312

  12. Kim M, Han J (2009) A Particle-and-Density based Evolutionary Clustering Method for Dynamic Networks. In Proceedings of the 35th International Conference on Very Large Data Bases (VLDB’09), 633–633

  13. Lahiri M, Berger-Wolf T (2007) Structure Prediction in Temporal Networks using Frequent Subgraphs. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM’07), 35–42.

  14. Lin Y, Chi Y, Zhu S, Sundaram H, Tseng B (2008) Facetnet: A Framework for Analyzing Communities and Their Evolutions in Dynamic Networks. In Proceedings of the 17th International Conference on the World Wide Web (WWW’08), 685–694

  15. Liu Z, Yu J, Ke Y, Lin X, Chen L (2008) Spotting significant changing subgraphs in evolving graphs. In Proceeding of the 8th IEEE International Conference on Data Mining (ICDM 2008), 917–922

  16. Palla G, Barabási A, Vicsek T (2007) Quantifying social group evolution. Nature 446:664–667

    Article  Google Scholar 

  17. Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal D, Hsu M (2004) Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng 16(11):1424–1440

    Article  Google Scholar 

  18. Qin G, Gao L, Yang J, Li J (2011) Evolution pattern discovery in dynamic networks. In: IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC’11)

  19. Wackersreuther B, Wackersreuther P, Oswald A, Bohm C, Borgwardt K (2010) Frequent subgraph discovery in dynamic networks. In: Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG’10), 155–162

  20. You C, Holder L, Cook D (2009) Learning patterns in the dynamics of biological networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09), 977–986

  21. Mobile01. http://www.mobile01.com

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan

    Guan-Yi Jheng & Hung-Ming Liang

  2. Department of Information Management, National Central University, Taoyuan City, Taiwan

    Yi-Cheng Chen

Authors
  1. Guan-Yi Jheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi-Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hung-Ming Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi-Cheng Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jheng, GY., Chen, YC. & Liang, HM. Evolution pattern mining on dynamic social network. J Supercomput 77, 6979–6991 (2021). https://doi.org/10.1007/s11227-020-03534-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03534-1

Keywords

Search

Navigation