Skip to main content

Social Influence Analysis

  • Living reference work entry
  • First Online:
Encyclopedia of Social Network Analysis and Mining

Synonyms

Social networks members; Social networks users

Glossary

Confounding variables:

Unknown variables exist (e.g., common location, gender, school, and several other external factors), which may cause friends to behave similarly with one another

Correlation factor:

Correlation between variables is a measure of how well the variables are related. The most common measure of correlation in statistics is the Pearson correlation

Edge-reversal test:

Reserves the direction of all edges. Social influence spreads in the direction specified by the edges of the graph, and hence reversing the edges should intuitively change the estimate of the correlation

Homophily:

A user in the social network tends to be similar to his/her connected neighbors

Induction:

An action of a user is triggered by an action of another user

Selection:

People tend to create relationships with other people who are already similar to them

Shuffle test:

Shuffles the activation time of users. It is based on the idea that...

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

Access this chapter

Institutional subscriptions

References

  • Anagnostopoulos A, Kumar R, Mahdian M (2008) Influence and correlation in social networks. In: KDD’08, Las Vegas, pp 7–15

    Google Scholar 

  • Bearden WO, Etzel MJ (1982) Reference group influence on product and brand purchase decisions. J Consum Res 9:183–194

    Article  Google Scholar 

  • Bond RM, Fariss CJ, Jones JJ, Kramer ADI, Marlow C, Settle JE, Fowler JH (2012) A 61-million-person experiment in social influence and political mobilization. Nature 489:295–298

    Article  Google Scholar 

  • Chen W, Wang Y, Yang S, (2009) Efficient influence maximization in social networks. In: KDD’09, Paris

    Google Scholar 

  • Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD’10, Washington, DC

    Google Scholar 

  • Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: KDD’08, Las Vegas, pp 160–168

    Google Scholar 

  • De Bruyn A, Gary LL (2004) A Multi-Stage model of word of mouth through electronic referrals. eBusiness Research Center Working Paper

    Google Scholar 

  • Domingos P, Richardson M (2001) Mining the network value of customers. KDD’01, San Francisco, pp 57–66

    Google Scholar 

  • Edelman (2008) Edelman trust barometer. http://www.edelman.com/trust/2008. Accessed 18 Sept 2012

  • Farrow H, Yuan YC (2011) Building stronger ties with alumni through Facebook to increase volunteerism and charitable giving. J Comput Mediat Commun 16(3):445–464

    Article  Google Scholar 

  • Fowler JH (2005) Trunout in a small world. In: Zuckerman AS (ed) The Social logic of politics: personal networks as contexts for political behavior. Temple University Press, Philadelphia, pp 269–287

    Google Scholar 

  • Fowler JH, Christakis NA (2008) The dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham heart study. Br Med J 337:a2338

    Article  Google Scholar 

  • Friedkin N (1998) A structural theory of social influence. Cambridge University Press, Cambridge. doi:10.1017/CBO9780511527524

    Book  Google Scholar 

  • Goyal A, Bonchi F, Lakshmanan LVS (2010) Learning influence probabilities in social networks. In: Proceedings of the proceedings of the third ACM international conference on Web search and data mining, New York. ACM, New York, pp 241–250

    Google Scholar 

  • Hoppe B, Reinelt C (2010) Leadersh Q 21:600–619

    Article  Google Scholar 

  • Icrossing (2008) iCrossing’s how America searches: health and wellness. Retrieved 10 May 2009

    Google Scholar 

  • Katona Z, Zubcsek PP, Sarvary M (2011) Network effects and personal influences: the diffusion of an online social network. J Mark Res 48(3):425–443

    Article  Google Scholar 

  • Kelman H (1958) Compliance, identification, and inter-nalization: three processes of attitude change. J Confl Resolut 2(1):51–60

    Article  Google Scholar 

  • Kelman HC (1961) Processes of opinion change. Public Opin Q 25:57–78

    Article  Google Scholar 

  • Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: KDD’03. Washington, DC, pp 137–146

    Google Scholar 

  • Kimura M, Saito K (2006) Tractable models for information diffusion in social networks. In: Proceedings of the 10th European conference on principles and practice of knowledge discovery in databases, Berlin, pp 259–271

    Google Scholar 

  • Krulwich B, Burkey C (1995) Contact finder: extracting indications of expertise and answering questions with referrals. In: Symposium on intelligent knowledge navigation and retrieval, Cambridge, pp 85–91

    Google Scholar 

  • Li H, Sourav SB, Sun A (2011) CASINO: towards conformity-aware social influence analysis in online social networks. In: Proceedings of the 20th ACM conference on information and knowledge management, CIKM 2011, Glasgow, October 24–28

    Google Scholar 

  • Mao Y, Xingjie L, Wang-Chien Lee (2012) Exploring social influence for recommendation – a generative model approach. In: Proceeding SIGIR’12 proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, Portland, pp 671–680

    Google Scholar 

  • Matsuo Y, Yamamoto H (2009) Community gravity: measuring bidirectional effects by trust and rating on online social networks. In: Proceedings of the 18th international conference on world wide web. Madrid. ACM, New York, pp 751–760

    Chapter  Google Scholar 

  • Park W, Lessig VP (1977) Students and housewives: differences in susceptibility to reference group influence. J Consum Res 4:102–110

    Article  Google Scholar 

  • Rainie L, Smith A (2012) Politics on social networking sites. Pew Research Center’s internet & American Life Project. http://pewinternet.org/~/media/Files/Reports/ 2012/PIP_PoliticalLifeonSocialNetworkingSites.pdf. Accessed 4 Sept 2012

  • Scott J (2000) Social network analysis: a handbook. Sage, London

    Google Scholar 

  • Singla P, Richardson M (2008) Yes, there is a correlation: from social networks to personal behavior on the web. In: WWW’08, Beijing, pp 655–664

    Google Scholar 

  • Tang J (2009) Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. Paris. ACM, New York, pp 807–816

    Chapter  Google Scholar 

  • Wang C, Tang J, Sun J, Han J (2011) Dynamic social influence analysis through time-dependent factor graphs. In: International conference on advances in social networks analysis and mining Kaohsiung, Taiwan, 25–27 July 2011, IEEE Computer Society, Los Alamitos

    Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Zhang J, Ackerman M, Adamic L (2007) Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th international conference on World Wide Web, Banff. ACM, New York, pp 221–230

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tiziana Guzzo .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this entry

Cite this entry

Guzzo, T., Ferri, F., Grifoni, P. (2017). Social Influence Analysis. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_186-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_186-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7163-9

  • Online ISBN: 978-1-4614-7163-9

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics