Skip to main content

The Independent Cascade and Linear Threshold Models

  • Chapter
Diffusion in Social Networks

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

In this chapter, we focus on perhaps the two most prevalent diffusion models in computer science—the independent cascade and linear threshold models. We describe different properties of these models and how these properties affect solving problems such as influence maximization and influence spread. We describe approaches to address influence maximization problem in independent cascade model and linear threshold model that rely on the maximization of submodular functions—as well as extensions to these approaches for larger datasets.

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

Access this chapter

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
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Kempe, David and Kleinberg, Jon and Tardos. Maximizing the spread of influence through a social network. (2003) ACM 137–146.

    Google Scholar 

  2. Nemhauser, George L and Wolsey, Laurence A and Fisher, Marshall L. An analysis of approximations for maximizing submodular set functions. (1978) 14(1) 265–294.

    MATH  MathSciNet  Google Scholar 

  3. Mossel, Elchanan and Roch, Sebastien. On the submodularity of influence in social networks. (2007) ACM 128–134.

    Google Scholar 

  4. Goyal, Amit and Lu, Wei and Lakshmanan, Laks VS. Simpath: An efficient algorithm for influence maximization under the linear threshold model.(2011) Data Mining (ICDM), 2011 IEEE 11th International Conference, 211–220.

    Google Scholar 

  5. Chen, Wei and Wang, Chi and Wang, Yajun. Scalable influence maximization for prevalent viral marketing in large-scale social networks. (2010) Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 1029–1038.

    Google Scholar 

  6. Shakarian, Paulo and Salmento, Joseph and Pulleyblank, William and Bertetto, John. Reducing gang violence through network influence based targeting of social programs. (2014) 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 1829–1836

    Google Scholar 

  7. Lu, Wei and Lakshmanan, Laks VS. Profit maximization over social networks. (2012) arXiv preprint arXiv:1210.4211

    Google Scholar 

  8. Minoux, Michel. Accelerated greedy algorithms for maximizing submodular set functions. (1978), Optimization Techniques 234–243.

    Google Scholar 

  9. Wang, Chi and Chen, Wei and Wang, Yajun. Scalable influence maximization for independent cascade model in large-scale social networks (2012) Data Mining and Knowledge Discovery. 545–576.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2015 The Author(s)

About this chapter

Cite this chapter

Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R. (2015). The Independent Cascade and Linear Threshold Models. In: Diffusion in Social Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-23105-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23105-1_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23104-4

  • Online ISBN: 978-3-319-23105-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics