Skip to main content

Diffusion of Information

  • Chapter
  • First Online:
  • 650 Accesses

Part of the book series: SpringerBriefs in Optimization ((BRIEFSOPTI))

Abstract

In this chapter, we outline the techniques used in optimizing or facilitating information diffusion in social networks. We identify two problem definitions through which a broad survey of techniques in recent research is provided. Namely, we explore the problems of maximizing the spread of influence and minimizing the spread of misinformation in social networks. As different as these problems are in terms of the motivation behind them, they both rely on sub-problems that are very similar. Through our study of these two problems, we delve into more detail about the sub-problems: Sect. 2.2 model formation, Sect. 2.3 problem optimization, Sect. 2.4 large-scale data analysis, and Sect. 2.5 research trends.

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   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

Learn about institutional subscriptions

References

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

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  3. F.M. Bass, A new product growth for model consumer durables. Manag. Sci. 15(5), 215–227 (1969)

    Article  Google Scholar 

  4. S. Bharathi, D. Kempe, M. Salek, Competitive influence maximization in social networks. Internet Netw. Econ. 4858, 306–311 (2007)

    Article  Google Scholar 

  5. C. Borgs, M. Brautbar, J. Chayes, B. Lucier, Maximizing social influence in nearly optimal time, in Proceedings of the Twenty-fifth Annual ACM-SIAM Symposium on Discrete Algorithms (2014), pp. 946–957

    Google Scholar 

  6. A. Borodin, Y. Filmus, J. Oren, Threshold models for competitive influence in social networks, in WINE (2010), pp. 539–550

    Google Scholar 

  7. C. Budak, D. Agrawal, A.E. Abbadi, Limiting the spread of misinformation in social networks. WWW (2011), pp. 665–674

    Google Scholar 

  8. T. Carnes, C. Nagarajan, S.M. Wild, A.V. Zuylen, Maximizing influence in a competitive social network: a follower’s perspective, in Proceedings of the 9th International Conference on Electronic Commerce (ICEC) (2007), pp. 351–360

    Google Scholar 

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

    Article  Google Scholar 

  10. W. Chen, Y. Wang, S. Yang, Efficient influence maximization in social networks, in KDD (2009), pp. 199–208

    Google Scholar 

  11. W. Chen, Y. Yuan, L. Zhang, Scalable influence maximization in social networks under the linear threshold model, in ICDM (2010), pp. 88–97

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  14. C.-T. Chu, S.K. Kim, Y.-A. Lin, Y. Yu, G.R. Bradski, A.Y. Ng, K. Olukotun, Map-reduce for machine learning on multicore, in Proceedings of the 19th Neural Information Processing Systems (NIPS) (2006), pp. 281–288

    Google Scholar 

  15. D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, S. Suri, 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) (2008), pp. 160–168

    Google Scholar 

  16. A. Das, M. Datar, A. Garg, S. Rajaram, Google news personalization: scalable online collaborative filtering, in Proceeding of the 16th International Conference on World Wide Web (WWW) (2007), pp. 271–280

    Google Scholar 

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

    Google Scholar 

  18. P. Domingos, M. Richardson, Mining the network value of customers, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2001), pp. 57–66

    Google Scholar 

  19. D. Easley, J. Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Cambridge University Press, Cambridge, 2010)

    Book  Google Scholar 

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

    Google Scholar 

  21. L.A. Fourt, J.W. Woodlock, Early prediction of market success for grocery products. J. Mark. 25, 31–38 (1960)

    Article  Google Scholar 

  22. J. Goldenberg, B. Libai, E. Muller, Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12, 211–223 (2001)

    Article  Google Scholar 

  23. A. Goyal, F. Bonchi, L.V. Lakshmanan, Learning influence probabilities in social networks, in Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (2010), pp. 241–250

    Google Scholar 

  24. A. Goyal, W. Lu, L. Lakshmanan, A data-based approach to social influence maximization. Proc. VLDB Endowment5(1), 73–84 (2011)

    Article  Google Scholar 

  25. M. Granovetter, Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)

    Article  Google Scholar 

  26. X. He, G. Song, W. Chen, Q. Jiang, Influence blocking maximization in social networks under the competitive linear threshold model, in SDM (2012)

    Google Scholar 

  27. H.W. Hethcote, The mathematics of infectious diseases. SIAM Rev. 42, 599–653 (2000)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  30. D. Kempe, J. Kleinberg, E. Tardos, Influential nodes in a diffusion model for social networks, in ICALP (2005), pp. 1127–1138

    Google Scholar 

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

    Google Scholar 

  32. J. Kostka, Y.A. Oswald, R. Wattenhofer, Word of mouth: Rumor dissemination in social networks, in SIROCCO (2008), pp. 185–196

    Google Scholar 

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

    Article  Google Scholar 

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

    Book  Google Scholar 

  35. T. Lappas, E. Terzi, D. Gunopulos, H. Mannila, Finding effectors in social networks, in KDD (2010)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  38. B. Liu, G. Cong, D. Xu, Y. Zeng, Time constrained influence maximization in social networks, in IEEE 12th International Conference on Data Mining (ICDM) (2012), pp. 439–448

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  40. V. Mahajan, E. Muller, F. Bass, New product diffusion models in marketing: a review and directions for research. J. Mark. 54, 1–26 (1990)

    Article  Google Scholar 

  41. E. Mansfield, Technical change and the rate of imitation. Econometrica 29, 741–766 (1961)

    Article  Google Scholar 

  42. M. Mathioudakis, F. Bonchi, C. Castillo, A. Gionis, A. Ukkonen, Sparsification of influence networks, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011), pp. 529–537

    Google Scholar 

  43. E. Morozov, Swine flu. Twitter’s power to misinform. Foreign Policy (2009)

    Google Scholar 

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

    Google Scholar 

  45. A. Mouravski, Influence maximization on families of graphs. Thesis, 2011

    Google Scholar 

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

    Google Scholar 

  47. N.P. Nguyen, G. Yan, M.T. Thai, S. Eidenbenz, Containment of misinformation spread in online social networks, in Proceedings of the 4th Annual ACM Web Science Conference (WebSci) (2012), pp. 213–222

    Google Scholar 

  48. H.T. Nguyen, M.T. Thai, T.N. Dinh, Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks, in Proceedings of the 2016 International Conference on Management of Data (2016), pp. 695–710

    Google Scholar 

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

    Google Scholar 

  50. K. Saito, R. Nakano, M. Kimura, Prediction of information diffusion probabilities for independent cascade model, in Knowledge-Based Intelligent Information and Engineering Systems (Springer, 2008), pp. 67–75

    Google Scholar 

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

    Google Scholar 

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

    Book  Google Scholar 

  53. P. Smolensky, Information processing in dynamical systems: Foundations of harmony theory, in Parallel Distributed Processing, ed. by D. E. Rumelhart, J. L. McClelland, vol. 1, (MIT Press, Cambridge, 1986), pp. 194–281

    Google Scholar 

  54. L. Sun, W. Huang, P.S. Yu, W. Chen, Multi-round influence maximization, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2018), pp. 2249–2258

    Google Scholar 

  55. J. Tang, J. Sun, C. Wang, Z. Yang, Social influence analysis in large-scale networks, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009), pp. 807–816

    Google Scholar 

  56. Y. Tang, X. Xiao, Y. Shi, Influence maximization: near-optimal time complexity meets practical efficiency, in Proceedings of SIGMOD International Conference on Management of Data (2014), pp. 75–86

    Google Scholar 

  57. Y. Tang, Y. Shi, X. Xiao, Influence maximization in near-linear time: a martingale approach, in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (2015), pp. 1539–1554

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  60. D. Westermana, P.R. Spenceb, B.V.D. Heide, 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 

  61. W. Xu, Z. Lu, W. Wu, Z. Chen, A novel approach to online social influence maximization. J. Soc. Netw. Anal. Min. (SNAM) 4(1), 153–164 (2014)

    Google Scholar 

  62. Q. Yan, S. Guo, D. Yang, Influence maximizing and local influenced community detection based on multiple spread model, in ADMA, Part II. LNAI 7121 (2011), pp. 82–95

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xu, W., Wu, W. (2020). Diffusion of Information. In: Optimal Social Influence. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-37775-5_2

Download citation

Publish with us

Policies and ethics