Skip to main content

Social Influence

  • Reference work entry
  • First Online:
  • 14 Accesses

Synonyms

Information cascade; Information diffusion; Information spread; Innovation diffusion

Definition

SocialFootnote 1 influence is the study of individuals being affected by their peers. The subject studies how one’s ideas, beliefs, or characteristics are influenced and formed by their family, friends, colleagues, acquaintances, etc. These influences in a large scale lead to so called information diffusion (aka information cascade) that explores the reactions of network entities against new objects and ideas as a result of the social influence they receive from their peers. The topic has been a popular subject of study in different fields including psychology, sociology, economics, and computer science.

Information diffusion explores how and to what extent a new object, called innovation, diffuses through societies. Innovations are ideas, information, products, behaviors, cultures, emotions, viruses, or other objects that are “perceived as new by an individual or other unit of...

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   4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   6,499.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    The author currently works at Google Inc., Mountain view, CA

Recommended Reading

  1. Chen W, Wang C, Wang Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2010. p. 1029–38.

    Google Scholar 

  2. Chen W, Wang Y, Yang S. Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2009. p. 199–208.

    Google Scholar 

  3. Domingos P, Richardson M. Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001. p. 57–66.

    Google Scholar 

  4. Eftekhar M, Ganjali Y, Koudas N. Information cascade at group scale. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2013. p. 401–9.

    Google Scholar 

  5. Eftekhar M, Koudas N, Ganjali Y. Bursty subgraphs in social networks. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining; 2013. p. 213–22.

    Google Scholar 

  6. Gomez-Rodriguez M, Leskovec J, Krause A. Inferring networks of diffusion and influence. ACM Trans Knowl Discov Data (TKDD). 2012;5(4):1–37.

    Article  Google Scholar 

  7. Hartline J, Mirrokni VS, Sundararajan M. Optimal marketing strategies over social networks. In: Proceedings of the 17th International World Wide Web Conference; 2008. p. 189–98.

    Google Scholar 

  8. Immorlica N, Kleinberg J, Mahdian M, Wexler T. The role of compatibility in the diffusion of technologies through social networks. In: Proceedings of the 8th ACM Conference on Electronic Commerce; 2007. p. 75–83.

    Google Scholar 

  9. Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence in a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003. p. 137–46.

    Google Scholar 

  10. Leskovec J, Huttenlocher D, Kleinberg J. Predicting positive and negative links in online social networks. In: Proceedings of the 19th International World Wide Web Conference; 2010. p. 641–50.

    Google Scholar 

  11. 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; 2007. p. 420–29.

    Google Scholar 

  12. Nisan N, Roughgarden T, Tardos E, Vazirani VV. Algorithmic game theory, chapter 24. Cambridge/New York: Cambridge University Press; 2007.

    Google Scholar 

  13. Rogers EM. Diffusion of innovations. New York: Simon and Schuster; 2010.

    Google Scholar 

  14. Schelling T. Micromotives and macrobehavior. Nueva York: W.W. Norton & Company; 1978.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Milad Eftekhar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Eftekhar, M. (2018). Social Influence. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80694

Download citation

Publish with us

Policies and ethics