Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Actionable Information in Social Networks, Diffusion of

  • Cindy Hui
  • William A. Wallace
  • Malik Magdon-Ismail
  • Mark Goldberg
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_286




Spread of an idea, product, or behavior in a social system

Social Ties

Connections between individuals on which information is passed


Information diffusion is the process whereby information spreads through a social system through interactions among its members. Actionable information refers to information that requires the individual members to make a decision or perform an action.


Diffusion can be described as “the process by which an innovation is communicated through certain channels over time among the members of a social system” (Rogers 1995). The innovations can be ideas, products, or behaviors but in general can refer to anything that is perceived as new or novel by the individuals of the social system. The innovations spread from originating sources to prospective users and individuals. Diffusion theories and models are used to describe how these...

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This material is based upon work sponsored by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 at Rensselaer Polytechnic Institute and by the Department of Homeland Security through the Command, Control, and Interoperability Center for Advanced Data Analysis Center of Excellence at Rutgers University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the US Government.


  1. Abrahamson E, Rosenkopf L (1997) Social network effects on the extent of innovation diffusion: a computer simulation. Organ Sci 8(3):289–309CrossRefGoogle Scholar
  2. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Pol Econ 100(5):992–1026CrossRefGoogle Scholar
  3. Chiang Y (2007) Birds of moderately different feathers: Bandwagon dynamics and the threshold heterogeneity of network neighbors. J Math Sociol 31(1):47–69 MATHzbMATHCrossRefGoogle Scholar
  4. Delre S, Jager W, Janssen M (2006) Diffusion dynamics in small-world networks with heterogeneous consumers. Comput Math Organ Theory 13(2):185–202zbMATHCrossRefGoogle Scholar
  5. Domingos P (2005) Mining social networks for viral marketing. IEEE Intell Syst 20(1):80–82 MathSciNetMathSciNetCrossRefGoogle Scholar
  6. Duan W, Chen Z, Liu Z, Jin W (2005) Efficient target strategies for contagion in scale-free networks. Phys Rev E 72(2):026133CrossRefGoogle Scholar
  7. Eubank S, Guclu H, Kumar V, Marathe M, Srinivasan A, Toroczkai Z, Wang N (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429:180–184CrossRefGoogle Scholar
  8. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223CrossRefGoogle Scholar
  9. Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443CrossRefGoogle Scholar
  10. Granovetter M (1983) The strength of weak ties: a network theory revisited. Sociol Theory 1:201–233CrossRefGoogle Scholar
  11. Hui C, Goldberg M, Magdon-Ismail M, Wallace WA (2008) Micro-simulation of diffusion of warnings. In: Fiedrich F, de Walle BV (eds) Proceedings of the 5th international conference on information systems for crisis response and management (ISCRAM2008), Washington, DC, pp 424–430Google Scholar
  12. Hui C, Goldberg M, Magdon-Ismail M, Wallace WA (2009a) On the weak ties hypothesis in the diffusion of warnings. In: 2009 North American Association for computational social and organizational science annual conference (NAACSOS 2009). Arizona State University, TempeGoogle Scholar
  13. Hui C, Magdon-Ismail M, Wallace WA, Goldberg M (2009b) The impact of changes in network structure on the diffusion of warnings. In: Proceedings of the workshop on analysis of dynamic networks at the SIAM international conference on data mining, SparksGoogle Scholar
  14. Hui C, Goldberg M, Magdon-Ismail M, Wallace WA (2010a) Agent-based simulation of the diffusion of warnings. In: Agent-directed simulation symposium (ADS’10), as part of the 2010 Spring simulation multi-conference (SpringSim’10), OrlandoGoogle Scholar
  15. Hui C, Goldberg M, Magdon-Ismail M, Wallace WA (2010b) Simulating the diffusion of information: an agent-based modeling approach. Special issue on agent-directed simulation. Int J Agent Technol Syst 2(3):31–46CrossRefGoogle Scholar
  16. Hui C, Magdon-Ismail M, Wallace WA, Goldberg M (2011a) Aborting a message flowing through social communities. In: Proceedings of the 3rd IEEE international conference on social computing (SocialCom 2011). MIT, BostonGoogle Scholar
  17. Hui C, Magdon-Ismail M, Wallace WA, Goldberg M (2011b) Effectiveness of information retraction. In: IEEE 1st international workshop on network science (NSW 2011), West Point, June 22–24Google Scholar
  18. Katz E (1957) The two-step flow of communication: an up-to-date report of an hypothesis. Public Opin Quart 21(1):61–78CrossRefGoogle Scholar
  19. Kelton K, Fleischmann KR, Wallace WA (2008) Trust in digital information. J Am Soc Inf Sci Technol 59(3):363–374CrossRefGoogle Scholar
  20. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International conference on knowledge discovery and data mining. ACM Press, Washington, DC, USA, pp 137–146Google Scholar
  21. Kempe D, Kleinberg J, Tardos É (2005) Influential nodes in a diffusion model for social networks. In: Proceedings of the 32nd international colloquium on automata, languages and programming (ICALP), Lisboa, PortugalCrossRefGoogle Scholar
  22. Leskovec J, Adamic LA, Huberman BA (2006a) The dynamics of viral marketing. In: Proceedings of the 7th ACM conference on electronic commerce (EC06). ACM Press, New York, pp 228–237Google Scholar
  23. Leskovec J, Krause A, Guestrin C (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose, pp 420–429Google Scholar
  24. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining. Edmonton, Canada, pp 61–70Google Scholar
  25. Rogers E (1995) Diffusion of innovations. Free Press, New YorkGoogle Scholar
  26. Song X, Tseng BL, Lin CY, Sun MT (2006) Personalized recommendation driven by information flow. In: 29th annual international ACM SIGIR conference on research and development in information retrieval, Seattle, WA, pp 509–516Google Scholar
  27. Strang D, Macy MW (2001) In search of excellence: fads, success stories, and adaptive emulation. Am J Sociol 107(1):147–182CrossRefGoogle Scholar
  28. Valente TW (1996) Social network thresholds in the diffusion of innovations. Soc Netw 18(1):69–89 MathSciNetCrossRefGoogle Scholar
  29. Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–5771 MATHMathSciNetMathSciNetzbMATHCrossRefGoogle Scholar

Recommended Reading

  1. Albert R, Jeong H, Barabasi A (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382CrossRefGoogle Scholar
  2. Bass F (2004) A new product growth for model consumer durables. Manag Sci 50(Supplement 12):1825–1832CrossRefGoogle Scholar
  3. Brown J, Reignen P (1987) Social ties and word-of-mouth referral behaviour. J Consum Res 14(3):350–362CrossRefGoogle Scholar
  4. Chen L, Carley K (2004) The impact of countermeasure propagation on the prevalence of computer viruses. IEEE Trans Syst Man Cybern B Cybern 34(2):823–833CrossRefGoogle Scholar
  5. Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on World Wide Web. ACM Press, New York, pp 491–501Google Scholar
  6. Hill S, Provost F, Volinsky C (2006) Network-based marketing: identifying likely adopters via consumer networks. Stat Sci 21(2):256–276 MATHMathSciNetMathSciNetzbMATHCrossRefGoogle Scholar
  7. Huckfeldt R, Sprague J (1991) Discussant effects on vote choice: intimacy, structure, and interdependence. J Polit 53(1):122–158CrossRefGoogle Scholar
  8. Java A, Kolari P, Finin T, Oates T (2006) Modeling the spread of influence on the blogosphere. In: Proceedings of the 15th international conference on World Wide Web, EdinburghGoogle Scholar
  9. Leskovec J, Singh A, Kleinberg J (2006b) Patterns of influence in a recommendation network. In: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), SingaporeCrossRefGoogle Scholar
  10. Macy M (1991) Chains of cooperation: threshold effects in collective action. Am Sociol Rev 56(6):730–747CrossRefGoogle Scholar
  11. Meyers LA, Newman M, Pourbohloul B (2006) Predicting epidemics on directed contact networks. J Theor Biol 240(3):400–418 MathSciNetMathSciNetCrossRefGoogle Scholar
  12. Morris S (2000) Contagion. Rev Econ Stud 67(1):57–78 MATHMathSciNetzbMATHCrossRefGoogle Scholar
  13. Valente T (1995) Network models of the diffusion of innovations. Hampton Press, CresskillGoogle Scholar
  14. Wan X, Yang J (2007) Learning information diffusion process on the web. In: Proceedings of the 16th international conference on World Wide Web. ACM Press, New York, pp 1173–1174CrossRefGoogle Scholar
  15. Young HP (2003) The diffusion of innovations in social networks. In: The economy as an evolving complex system, III. Oxford University Press, OxfordGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Cindy Hui
    • 1
  • William A. Wallace
    • 1
  • Malik Magdon-Ismail
    • 2
  • Mark Goldberg
    • 2
  1. 1.Department of Industrial and Systems EngineeringRensselaer Polytechnic InstituteTroyUSA
  2. 2.Department of Computer ScienceRensselaer Polytechnic InstituteTroyUSA

Section editors and affiliations

  • Rosa M. Benito
    • 1
  • Juan Carlos Losada
    • 2
  1. 1.Universidad Politécnica de MadridMadridSpain
  2. 2.Universidad Politécnica de MadridMadridSpain