Skip to main content
Springer Nature Link
Log in

Social Network Analytics: Beyond the Obvious

  • Conference paper
  • First Online:
Biomedical Data Management and Graph Online Querying (Big-O(Q) 2015, DMAH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9579))

  • 822 Accesses

Abstract

Over the last decade and a half, there has been an explosion of interest in the analysis and mining of very large networks, including those arising in social and information networks, biological networks such as protein-protein interaction networks, web graph, collaboration networks, customer-product interaction networks, and road networks, to name a few. The interest has been fueled by driving applications as well as by the unprecedented availability of real network data. Of particular note among the driving applications are the study of spread of infections and innovations, word-of-mouth marketing or the so-called viral marketing, and tracking of events and stories in social media. In this talk, I will use viral marketing and event and story evolution tracking as concrete settings with which to describe some exciting research that has been done over the past several years. In the process, I will briefly discuss some less obvious applications of mining large graphs.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agarwal, M.K., Ramamritham, K., Bhide, M.: Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. PVLDB 5(10), 980–991 (2012)

    Google Scholar 

  2. Angel, A., Koudas, N., Sarkas, N., Srivastava, D.: Dense subgraph maintenance under streaming edge weight updates for real-time story identification. PVLDB 5(6), 574–585 (2012)

    Google Scholar 

  3. Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: SDM (2006)

    Google Scholar 

  4. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)

    Google Scholar 

  5. Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: ICDM, pp. 88–97 (2010)

    Google Scholar 

  6. Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: KDD, pp. 133–142 (2007)

    Google Scholar 

  7. Cohen, E., Delling, D., Pajor, T., Werneck, R.F.: Sketch-based influence maximization and computation: scaling up with guarantees. In: CIKM (2014)

    Google Scholar 

  8. Durrett, R.: Lecture Notes on Particle Systems and Percolation. Wadsworth Publishing, California (1988)

    MATH  Google Scholar 

  9. Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. PVLDB 5(1), 73–84 (2011)

    Google Scholar 

  10. Goyal, A., Lu, W., Lakshmanan, L.V.S.: Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: WWW (Companion Volume), pp. 47–48 (2011)

    Google Scholar 

  11. Goyal, A., Lu, W., Lakshmanan, L.V.S.: Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: ICDM, pp. 211–220 (2011)

    Google Scholar 

  12. Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)

    Article  Google Scholar 

  13. Gruhl, D., Guha, R.V., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web (WWW 2004) (2004)

    Google Scholar 

  14. Jung, K., Heo, W., Chen, W.: IRIE: scalable and robust influence maximization in social networks. In: ICDM, pp. 918–923 (2012)

    Google Scholar 

  15. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)

    Google Scholar 

  16. Kim, M.-S., Han, J.: A particle-and-density based evolutionary clustering method for dynamic networks. PVLDB 2(1), 622–633 (2009)

    Google Scholar 

  17. Leskovec, J., Backstrom, L., Kleinberg, J.M.: Meme-tracking and the dynamics of the news cycle. In: KDD, pp. 497–506 (2009)

    Google Scholar 

  18. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM (2007)

    Google Scholar 

  19. Liggett, T.: Interacting Particle Systems. Springer, New York (1985)

    Book  MATH  Google Scholar 

  20. Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Tweets as data: demonstration of tweeql and twitinfo. In: SIGMOD Conference, pp. 1259–1262 (2011)

    Google Scholar 

  21. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions - i. Math. Program. 14(1), 265–294 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  22. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: WWW, pp. 851–860 (2010)

    Google Scholar 

  23. Schelling, T.: Micromotives and Macrobehavior. Norton (1978)

    Google Scholar 

  24. Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, pp. 1539–1554. ACM, New York (2015)

    Google Scholar 

  25. Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: SIGMOD (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of British Columbia, Vancouver, BC, Canada

    Laks V. S. Lakshmanan

Authors
  1. Laks V. S. Lakshmanan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laks V. S. Lakshmanan .

Editor information

Editors and Affiliations

  1. Stony Brook University, Stony Brook, New York, USA

    Fusheng Wang

  2. University of Utah, Salt Lake City, Utah, USA

    Gang Luo

  3. Columbia University, New York, New York, USA

    Chunhua Weng

  4. Nanyang Technological University, Singapore, Singapore

    Arijit Khan

  5. Qatar Computing Research Institute, Doha, Qatar

    Prasenjit Mitra

  6. Google Research, New York, New York, USA

    Cong Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Lakshmanan, L.V.S. (2016). Social Network Analytics: Beyond the Obvious. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41576-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41575-8

  • Online ISBN: 978-3-319-41576-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics