Skip to main content
SpringerLink
Log in

Influence Diffusion in Social Networks

  • Chapter
  • First Online:
Optimization in Science and Engineering

Abstract

Recently influence diffusion in social networks has become a hot topic in research communities. In this paper, we outline the techniques used in optimizing or facilitating information diffusion in social networks. We begin with an overview of social networks identifying its significance and characteristics. Then among various problems that are related to diffusion of information in social networks, we study the two fundamental problems using multi-scale analysis, namely (a) maximizing the influence spread and (b) minimizing the spread of misinformation in social networks. Research trends of this topic are also detected and discussed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Institutional subscriptions

References

  1. Hughes, A.L., Palen, L.: Twitter adoption and use in mass convergence and emergency events. In: Proceedings of the 6th International Information Systems for Crisis Response and Management Conference (2009)

    Google Scholar 

  2. Grossman, L.: Iran protests: Twitter, the medium of the movement. Time (online) (June 2009). http://www.time.com/time/world/article/0,8599,1905125,00.html

  3. Smith, C.: Egypt’s facebook revolution: Wael ghonim thanks the social network. The Huffington Post, February 2011. http://www.huffingtonpost.com/2011/02/11/egypt-facebook-revolution-wael-ghonim_n_822078.html

  4. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM), Cambridge, August 1999

    Google Scholar 

  5. Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  6. Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the web for emerging cyber-communities. Comput. Netw. 31, 1481–1493 (1999)

    Article  Google Scholar 

  7. Adamic, L.A., Buyukkokten, O., Adar, E.: A social network caught in the Web. First Monday 8(6), 35–42 (2003)

    Article  Google Scholar 

  8. Braitenberg, V., Schüz, A.: Anatomy of a Cortex: Statistics and Geometry. Springer, Berlin (1991)

    Book  Google Scholar 

  9. Phadke, A.G., Thorp, J.S.: Computer relaying for power systems. Wiley, New York (1988)

    Google Scholar 

  10. Li, L., Alderson, D., Doyle, J.C., Willinger, W.: Towards a theory of scale-free graphs: definitions, properties, and implications. Internet Math. 2(4), 431–523 (2006)

    Article  MathSciNet  Google Scholar 

  11. Albert, R., Jeong, H., Barabasi, A.L.: The diameter of the world wide web. Nature 401, 130 (1999)

    Article  Google Scholar 

  12. Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph structure in the web: Experiments and models. In: Proceedings of the 9th International World Wide Web Conference (WWW), Amsterdam, May 2000

    Google Scholar 

  13. Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. (PNAS) 98, 409–415 (2001)

    Google Scholar 

  14. Amaral, L.A.N., Scala, A., Barthelemy, M., Stanley, H.E.: Classes of small-world networks. Proc. Natl. Acad. Sci. (PNAS) 97, 11149–11152 (2000)

    Google Scholar 

  15. Kleinberg, J.: The small-world phenomenon: An algorithmic perspective. In: Proceedings of the 32nd ACM Symposium on Theory of Computing (STOC), Portland, May 2000

    Google Scholar 

  16. Kleinberg, J.: Navigation in a small world. Nature 406, 845–845 (2000)

    Article  Google Scholar 

  17. Milgram, S.: The small world problem. Psychol. Today, 2(60), 60–67 (1967)

    Google Scholar 

  18. Pool, I., Kochen, M.: Contacts and influence. Soc. Netw. 1, 1–48 (1978)

    Google Scholar 

  19. Granovetter, M.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)

    Article  Google Scholar 

  20. Wasserman, S., Faust, K.: Social Networks Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)

    Book  Google Scholar 

  21. Siganos, G., Tauro, S.L., Faloutsos, M.: Jellyfish: A conceptual model for the AS internet topology. J. Commun. Netw. 8(3), 339–350 (2006)

    Article  Google Scholar 

  22. Kleinberg, J., Lawrence, S.: The structure of the web. Science 294, 1849–1850 (2001)

    Article  Google Scholar 

  23. Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46, 604–632 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  24. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford University (1998)

    Google Scholar 

  25. Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., Tomkins, A.: Geographic routing in social networks. Proc. Natl. Acad. Sci. (PNAS) 102(33), 11623–11628 (2005)

    Google Scholar 

  26. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Philadelphia, August 2006

    Google Scholar 

  27. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. (PNAS) 99, 7821–7826 (2002)

    Google Scholar 

  28. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: Membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Philadelphia, August 2006

    Google Scholar 

  29. Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010)

    Article  Google Scholar 

  30. Anderson, R.M., May, R.M.: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford (1992)

    Google Scholar 

  31. Schumpeter, J., Bakhays, U.: The Theory of Economics Development. Springer, New York (2003)

    Google Scholar 

  32. Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  33. Westermana, D., Spenceb, P.R., Heide, B.V.D.: A social network as information: The effect of system generated reports of connectedness on credibility on twitter. Comput. Hum. Behav. 28(1), 199–206 (2012)

    Article  Google Scholar 

  34. Lopez-Pintado, D.: Diffusion in complex social networks. Games Econ. Behav. 62(2), 573–590 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  35. Meier, D., Oswald, Y.A., Schmid, S., Wattenhofer, R.: On the windfall of friendship: Inoculation strategies on social networks. In: ICEC, pp. 294–301 (2008)

    Google Scholar 

  36. Salathé, M., Jones, J.H.: Dynamics and control of diseases in networks with community structure. PLoS Comput. Biol. 6(8), e1000736 (2010). doi:10.1371/journal.pcbi.1000736

    Article  Google Scholar 

  37. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)

    Google Scholar 

  38. Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedingsof the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  39. Kempe, D., Kleinberg, J., Tardos, E.: Influential nodes in a diffusion model for social networks. In: ICALP, pp. 1127–1138. Springer, New York (2005)

    Google Scholar 

  40. Mossel, E., Roch, S.: On the submodularity of influence in social networks. In: Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing (STOC), p. 128 (2007)

    Google Scholar 

  41. 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  MATH  MathSciNet  Google Scholar 

  42. Lu, Z., Zhang, W., Wu, W., Kim, J., Fu, B.: The complexity of influence maximization problem in the deterministic linear threshold model. J. Comb. Optim. 24(3), 374–378 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  43. Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of influence networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’11), pp. 529–537, New York, USA (2011)

    Google Scholar 

  44. 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 (KDD’10), pp. 1029-1038, New York, USA (2010)

    Google Scholar 

  45. Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM’10), pp. 88–97 (2010)

    Google Scholar 

  46. Kimura, M., Saito, K.: Tractable models for information diffusion in social networks. In: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 259–271 (2006)

    Google Scholar 

  47. Saito, K., et al.: Prediction of information diffusion probabilities for independent cascade model. In: Knowledge-Based Intelligent Information and Engineering Systems (KES’08), Lecture Notes in Computer Science, 5179, 67–75 (2008)

    Article  Google Scholar 

  48. Goyal, A., Lu, W., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. In: PVLDB 5(1), 73–84 (2011)

    Google Scholar 

  49. Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining (WSDM’10), pp. 241–250, New York, USA (2010)

    Google Scholar 

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

    Google Scholar 

  51. Lappas, T., Terzi, E., Gunopulos, D., Mannila, H.: Finding effectors in social networks. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’10), pp. 1059–1068, New York, USA (2010)

    Google Scholar 

  52. Carnes, T., Nagarajan, C., Wild, S.M., van Zuylen, A.: Maximizing influence in a competitive social network: A follower’s perspective. In: Proceedings of the 9th International Conference on Electronic Commerce (ICEC) (2007)

    Google Scholar 

  53. Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Proceedings of the 3rd international conference on Internet and network economics (WINE’07), 4858, 306–311 (2007)

    Google Scholar 

  54. Kostka, J., Oswald, Y.A., Wattenhofer, R.: Word of mouth: Rumor dissemination in social networks. In: Structural Information and Communication Complexity (SIROCCO’08), Lecture Notes in Computer Science, 5058, 185–196 (2008)

    Article  MathSciNet  Google Scholar 

  55. Trpevski, D., Tang, W.K.S., Kocarev, L.: Model for rumor spreading over networks. Phys. Rev. E, 81(5), 056102 (2010)

    Article  Google Scholar 

  56. Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: Proceedings of the 6th international conference on Internet and network economics (WINE’10), pp. 539–550 (2010)

    Google Scholar 

  57. Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincn, D., Sun, X., Wang, Y., Wei, W., Yuan, Y.: Influence maximization in social networks when negative opinions may emerge and propagate. In: SIAM Data Mining (SDM), pp. 379–390 (2011)

    Google Scholar 

  58. Dubey, P., Garg, R., Meyer, B.D.: Competing for customers in a social network: The quasi-linear case. Internet Netw. Econ. 4286, 162–173 (2006)

    Article  Google Scholar 

  59. Morozov, E.: Swine flu. Twitter’s power to misinform. Foreign Policy, April 2009. http://neteffect.foreignpolicy.com/posts/2009/04/25/swine_flu_twitters_power_to_misinform

  60. Heussner, K.M.: Enough already! 7 twitter hoaxes and half-truths. ABC News (January 2010)

    Google Scholar 

  61. Fan, L., Lu Z., Wu W., Thuraisingham B., Ma H., and Bi Y.: Least Cost Rumor Blocking in Social Networks. In: Proceedings of the 33rd Internaltional Conference on Distributed Computing Systems (ICDCS), pp. 540–549 (2013)

    Google Scholar 

  62. Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (2008)

    Google Scholar 

  63. Budak, C., Agrawal, D., Abbadi, A.E.: Limiting the spread of misinformation in social networks. In: International World Wide Web Conference (WWW’11), March 28–April 1, Hyderabad, India, pp. 665–674 (2011)

    Google Scholar 

  64. He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SIAM Data Mining (SDM), pp. 463–474 (2012)

    Google Scholar 

  65. Nguyen, N.P., Yan, G., Thai, M.T., Eidenbenz, S.: Containment of Misinformation Spread in Online Social Networks. In: Proceedings of the 3rd Annual ACM Web Science Conference (WebSci’12), pp. 213–222, ACM New york, USA (2012)

    Google Scholar 

  66. C. for Computational Analysis of Social and O. S. (CASOS). Casos networks. http://www.casos.cs.cmu.edu/computational_tools/data2.php (2005)

  67. Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? http://an.kaist.ac.kr/traces/WWW2010.html (2010)

  68. Leskovec, J.: Stanford large network dataset collection. http://snap.stanford.edu/data/index.html (2009)

  69. Newman, M.: Network data. http://www-personal.umich.edu/~mejn/netdata/ (2013)

  70. Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: Scalable online collaborative filtering. In: Proceeding of the 16th International Conference on World Wide Web (WWW) (2007)

    Google Scholar 

  71. Chu, C.-T., Kim, S.K., Lin, Y.-A., Yu, Y., Bradski, G.R., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. In: Proceedings of the 18th Neural Information Processing Systems (NIPS) (2006)

    Google Scholar 

  72. Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1, 194–281. MIT Press/Bradford Books, Cambridge (1986)

    Google Scholar 

  73. Kschischang, F.R., Member, S., Frey, B.J., Andrea Loeliger, H.: Factor graphs and the sum-product algorithm. IEEE Trans. Inf. Theory 47, 498–519 (2001)

    Article  MATH  Google Scholar 

  74. Welling, M., Hinton, G.E.: A new learning algorithm for mean field boltzmann machines. In: Proceedings of International Conference on Artificial Neural Network (ICANN), pp. 351–357 (2001)

    Google Scholar 

  75. Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: KDD (2009)

    Google Scholar 

  76. Yan, Q., Guo, S., Yang, D.: Influence maximizing and local influenced community detection based on multiple spread model. In: Advanced Data Mining and Applications (ADMA’11), Part II, LNAI 7121, pp. 82–95, (2011).

    Google Scholar 

  77. Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 160–168 (2008)

    Google Scholar 

  78. Saito, K., Ohara, K., Yamagishi, Y., Kimura, M., Motoda, H.: Learning diffusion probability based on node attributes in social networks. In: ISMIS, pp. 153–162 (2011)

    Google Scholar 

  79. Rodriguez, M.G., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. In: ICML, pp. 561–568 (2011)

    Google Scholar 

  80. Doerr, B., Fouz, M., Friedrich, T.: Social networks spread rumors in sublogarithmic time. In: Proceedings of the 43rd Annual ACM Symposium on Theory of Computing, pp. 21–30 (2011)

    Google Scholar 

  81. Fountoulakis, N., Panagiotouy, K., Sauerwaldz, T.: Ultra-fast rumor spreading in social networks. In: Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1642–1660 (2012)

    Google Scholar 

  82. Chen, W., Lu, W., Zhang, N.: Time-critical influence maximization in social networks with time-delayed diffusion process. In: AAAI, pp. 1–5 (2012)

    Google Scholar 

  83. Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: IEEE International Conference on Data Mining (ICDM), December 2012

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Texas at Dallas, 800 W. Campbell Road, MS EC31, Richardson, TX, 75080, USA

    Wen Xu, Weili Wu, Lidan Fan, Zaixin Lu & Ding-Zhu Du

Authors
  1. Wen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weili Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lidan Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zaixin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ding-Zhu Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen Xu .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Technical University of Athens, Athens, Greece

    Themistocles M. Rassias

  2. Princeton University Dept. Chemical Engineering, Princeton, New Jersey, USA

    Christodoulos A. Floudas

  3. Dept. Industrial & Systems Engineering, Texas A&M University, College Station, Texas, USA

    Sergiy Butenko

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Xu, W., Wu, W., Fan, L., Lu, Z., Du, DZ. (2014). Influence Diffusion in Social Networks. In: Rassias, T., Floudas, C., Butenko, S. (eds) Optimization in Science and Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0808-0_27

Download citation

Publish with us

Policies and ethics

Search

Navigation