Skip to main content
Springer Nature Link
Log in

Differential Information Diffusion Model in Social Network

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10751))

Included in the following conference series:

  • 3387 Accesses

Abstract

A social network is modeled as a graph of nodes connected through interactions among users, an important medium for the information spreading and influence on users dynamically. Modeling information diffusion is still key problem to predict influences of information on users. In recent years, numerous literatures have proposed models to solve this problem. However, each of models is coming from different points of view. Based on differential equations and with a real mechanism of transferring, exchanging information in network, in this paper, it is proposed a model of temporal-spatial information diffusion, named differential information diffusion or DID model. This model is setup in accordance with topological structure of network, semantic content and interactive activities of users in network. Experimental computations show the feasibility of the proposed model, conformity with network topology and with prospects of scalability for large networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Rogers, E.M.: Diffusion of Innovations. Free Press, New York (1962)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  4. Guille, A., Hacid, H., Favre, C., Zighed, D.: Information diffusion in online social networks: a survey. SIGMOD Rec. 42, 17–28 (2013)

    Article  Google Scholar 

  5. Kermack, M.: Contributions to the mathematical theory of epidemics. In: Royal Society of Edinburgh, Section A: Mathematics, vol. 115 (1972)

    Google Scholar 

  6. Lei, C., Lin, Z., Wang, H.: The free boundary problem describing information diffusion in online social networks. J. Differ. Equ. 254, 1326–1341 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Pinto, J.C.L.: Information diffusion and opinion dynamics in social networks. Doctor thesis NNT: 2016TELE0001, Pierre and Marie Curie University, Paris, France (2016)

    Google Scholar 

  8. Woo, J., Chen, H.: Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. Springer-Open J. Springer-Plus 5, 66 (2016). https://doi.org/10.1186/s40064-016-1675

    Article  Google Scholar 

  9. Wang, H., Wang, F., Xu, K.: Modeling information diffusion in online social networks with partial differential equations. arXiv1310.0505.v1, CS-SI 1 (2013)

  10. Yang, H., King, I., Lyu, M.R.: DiffusionRank: possible penicillin for Web spamming. In: Proceedings of the ACM SIGIR Conference, pp. 431–438 (2007)

    Google Scholar 

  11. Ma, H., Yang, H., Lyu, M.R., King, I.: Mining social networks using heat diffusion processes for marketing candidate selection. In: CIKM (2008)

    Google Scholar 

  12. Doo, M.: Spatial and social diffusion of information and Influence: models and algorithms. Ph.D. thesis. Georgia Institute of Technology, Georgia, USA (2012)

    Google Scholar 

  13. Dieudonné, J.: Calcul Infinitésimal. Chapitre XII, Hermann éditeurs des Sciences et des Arts, pp. 381–418, Imprimé en France, Durant Paris (1986)

    Google Scholar 

  14. Liua, J., Xiong, Q., Shi, W., Shi, X., Wang, K.: Evaluating the importance of nodes in complex networks. Sci.-Direct Phy. A 452, 209–219 (2016)

    Google Scholar 

  15. Kuo, T.: A modified TOPSIS with a different ranking index. Eur. J. Oper. Res. 260, 152–160 (2017)

    Article  MathSciNet  Google Scholar 

  16. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391 (1990)

    Article  Google Scholar 

  17. Tu, H.T., Phan, T.T., Nguyen, K.P.: An adaptive latent semantic analysis for text mining. In: The IEEE International Conference on System Science and Engineering – ICSSE 2017. IEEE Xplore, HCM City Vietnam (2017)

    Google Scholar 

  18. Tu, H.T., Nguyen, K.P.: Kernel based similarity and discovering documents of similar interests. The 13th IEEE International Conference on: Natural Computation, Fuzzy Systems and Knowledge Discovery - ICNC-FSKD 2017, Guilin, China (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. HCMC University of Technology and Education, Thu Duc, Ho Chi Minh City, Vietnam

    Hong T. Tu

  2. University of Information Technology, VNU-HCM, Thu Duc, Ho Chi Minh City, Vietnam

    Khu P. Nguyen

Authors
  1. Hong T. Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khu P. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hong T. Tu or Khu P. Nguyen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Quang Binh University, Dong Hoi City, Vietnam

    Duong Hung Hoang

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Rutgers University, Piscataway, New Jersey, USA

    Hoang Pham

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tu, H.T., Nguyen, K.P. (2018). Differential Information Diffusion Model in Social Network. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75417-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75416-1

  • Online ISBN: 978-3-319-75417-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics