Skip to main content

GparMiner: A System to Mine Graph Pattern Association Rules

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11448))

Included in the following conference series:

Abstract

With the rapid development, social network analysis has been receiving significant attention. One popular direction in the filed is to mine graph-pattern association rules (\(\mathsf {GPARs}\)). In the demo, we present \(\mathsf {GparMiner}\), a system for mining \(\mathsf {GPARs}\), on big and distributed social networks. The system has following characteristics: (1) it supports parallel mining computation, to handle sheer size of real-life social networks; (2) it provides graphical interface to help users monitor the mining progress and have a better understanding of \(\mathsf {GPARs}\).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Facebook statistics; second quater (2018). https://www.statista.com/topics/751/facebook/

  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)

    Article  Google Scholar 

  3. Elseidy, M., Abdelhamid, E., Skiadopoulos, S., Kalnis, P.: GRAMI: frequent subgraph and pattern mining in a single large graph. PVLDB 7(7), 517–528 (2014)

    Google Scholar 

  4. Fan, W., Wang, X., Wu, Y., Xu, J.: Association rules with graph patterns. PVLDB 8, 1502–1513 (2015)

    Google Scholar 

  5. Namaki, M.H., Wu, Y., Song, Q., Lin, P., Ge, T.: Discovering temporal graph association rules. In: CIKM (2017)

    Google Scholar 

  6. Wang, X., Xu, Y.: Mining graph pattern association rules. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11030, pp. 223–235. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98812-2_19

    Chapter  Google Scholar 

  7. Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: ICDM (2002)

    Google Scholar 

Download references

Acknowledgement

Xin Wang is supported in part by the NSFC 71490722,71472185, and Fundamental Research Funds for the Central Universities, China.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xin Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, X., Xu, Y., Zhao, R., Lin, J., Zhan, H. (2019). GparMiner: A System to Mine Graph Pattern Association Rules. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_84

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18590-9_84

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18589-3

  • Online ISBN: 978-3-030-18590-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics