Skip to main content

sonSchema: A Conceptual Schema for Social Networks

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8217))

Abstract

sonSQL is a MySQL variant that aims to be the default database system for social network data. It uses a conceptual schema called sonSchema to translate a social network design into logical tables. This paper introduces sonSchema, shows how it can be instantiated, and illustrates social network analysis for sonSchema datasets. Experiments show such SQL-based analysis brings insight into community evolution, cluster discovery and action propagation.

This research was supported in part by MOE Grant No. R-252-000-394-112 and carried out at the SeSaMe Centre, which is supported by the Singapore NRF under its IRC@SG Funding Initiative and administered by the IDMPO.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andersen, R., Chung, F., Lang, K.: Local graph partitioning using pagerank vectors. In: FOCS, pp. 475–486 (2006)

    Google Scholar 

  2. Angles, R., Gutiérrez, C.: Survey of graph database models. Comput. Surv. 40(1) (2008)

    Google Scholar 

  3. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proc. KDD, pp. 44–54 (2006)

    Google Scholar 

  4. Bao, Z., Tay, Y.C., Zhou, J.: A conceptual schema for social networks, http://sonsql.comp.nus.edu.sg/rsn.pdf

  5. Bao, Z., Zeng, Y., Tay, Y.C.: sonLP: Social network link prediction by principal component regression. In: Proc. ASONAM (to appear, 2013)

    Google Scholar 

  6. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E, 1–6 (2004)

    Google Scholar 

  7. Cluet, S., Moerkotte, G.: On the complexity of generating optimal left-deep processing trees with cross products. In: Vardi, M.Y., Gottlob, G. (eds.) ICDT 1995. LNCS, vol. 893, pp. 54–67. Springer, Heidelberg (1995)

    Chapter  Google Scholar 

  8. Flake, G.W., Tarjan, R.E., Tsioutsiouliklis, K.: Graph clustering and minimum cut trees. Internet Mathematics 1(4) (2003)

    Google Scholar 

  9. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM, pp. 241–250 (2010)

    Google Scholar 

  10. Kannan, R., Vempala, S., Vetta, A.: On clusterings: Good, bad and spectral. J. ACM 51(3), 497–515 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kashima, H., Abe, N.: A parameterized probabilistic model of network evolution for supervised link prediction. In: Proc. ICDM, pp. 340–349 (2006)

    Google Scholar 

  12. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: WWW, pp. 695–704 (2008)

    Google Scholar 

  13. Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proc. WWW, pp. 631–640 (2010)

    Google Scholar 

  14. Levene, M., Loizou, G.: Why is the snowflake schema a good data warehouse design? Inf. Syst. 28(3), 225–240 (2003)

    Article  Google Scholar 

  15. Liben-Nowell, D., Kleinberg, J.M.: The link-prediction problem for social networks. JASIST 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  16. Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proc. KDD, pp. 243–252 (2010)

    Google Scholar 

  17. Ronen, R., Shmueli, O.: SoQL: A language for querying and creating data in social networks. In: Proc. ICDE, pp. 1595–1602 (2009)

    Google Scholar 

  18. Rys, M.: Scalable SQL. Commun. ACM 54(6), 48–53 (2011)

    Article  Google Scholar 

  19. Shakkottai, S., Ying, L., Sah, S.: Targeted coupon distribution using social networks. SIGMETRICS Perf. Eval. Rev. 38, 26–30 (2011)

    Article  Google Scholar 

  20. Spiliopoulou, M.: Evolution in social networks: A survey. In: Social Network Data Analytics, pp. 149–175. Springer (2011)

    Google Scholar 

  21. Stonebraker, M., Cattell, R.: 10 rules for scalable performance in ‘simple operation’ datastores. Commun. ACM 54, 72–80 (2011)

    Article  Google Scholar 

  22. Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proc. KDD, pp. 807–816 (2009)

    Google Scholar 

  23. Trißl, S., Leser, U.: Fast and practical indexing and querying of very large graphs. In: Proc. SIGMOD, pp. 845–856 (2007)

    Google Scholar 

  24. Tsvetovat, M., Diesner, J., Carley, K.: NetIntel: A database for manipulation of rich social network data. Technical Report CMU-ISRI-04-135, Carnegie Mellon University (2005)

    Google Scholar 

  25. Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: User interactions in social networks and their implications. In: Proc. EuroSys, pp. 205–218 (2009)

    Google Scholar 

  26. Zaiane, O.R., Chen, J., Goebel, R.: DBconnect: mining research community on DBLP data. In: Proc. WebKDD/SNA-KDD, pp. 74–81 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bao, Z., Tay, Y.C., Zhou, J. (2013). sonSchema: A Conceptual Schema for Social Networks. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds) Conceptual Modeling. ER 2013. Lecture Notes in Computer Science, vol 8217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41924-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41924-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41923-2

  • Online ISBN: 978-3-642-41924-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics