[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Real-Time Topic-Aware Influence Maximization Using Preprocessing

  • Conference paper
  • First Online:
Computational Social Networks (CSoNet 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9197))

Included in the following conference series:

Abstract

Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aslay, C., Barbieri, N., Bonchi, F., Baeza-Yates, R.: Online topic-aware influence maximization queries. In: EDBT (2014)

    Google Scholar 

  2. Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. In: ICDM (2012)

    Google Scholar 

  3. Bhagat, S., Goyal, A., Lakshmanan, L.V.S.: Maximizing product adoption in social networks. In: WSDM (2012)

    Google Scholar 

  4. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and Isdn Systems 30, 107–117 (1998)

    Article  Google Scholar 

  5. Budak, C., Agrawal, D., Abbadi, A.E.: Limiting the spread of misinformation in social networks. In: WWW (2011)

    Google Scholar 

  6. Chen, W., Lakshmanan, L.V., Castillo, C.: Information and Influence Propagation in Social Networks, vol. 5. Morgan & Claypool (2013)

    Google Scholar 

  7. Chen, W., Lin, T., Yang, C.: Real-time topic-aware influence maximization using preprocessing (2014). arXiv preprint arXiv:1403.0057

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

    Google Scholar 

  9. Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD (2001)

    Google Scholar 

  10. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM (2010)

    Google Scholar 

  11. Goyal, A., Lu, W., Lakshmanan, L.V.: Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: WWW (2011)

    Google Scholar 

  12. Goyal, A., Lu, W., Lakshmanan, L.V.: Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: ICDM (2011)

    Google Scholar 

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

    Google Scholar 

  14. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD (2003)

    Google Scholar 

  15. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: KDD (2007)

    Google Scholar 

  16. Lin, C.X., Mei, Q., Han, J., Jiang, Y., Danilevsky, M.: The joint inference of topic diffusion and evolution in social communities. In: ICDM (2011)

    Google Scholar 

  17. Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM (2010)

    Google Scholar 

  18. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)

    Google Scholar 

  19. Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

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

    Google Scholar 

  21. Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale social networks. DMKD 25(3), 545–576 (2012)

    MATH  Google Scholar 

  22. Wang, C., Yu, X., Li, Y., Zhai, C., Han, J.: Content coverage maximization on word networks for hierarchical topic summarization. In: CIKM (2013)

    Google Scholar 

  23. Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: WSDM (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tian Lin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chen, W., Lin, T., Yang, C. (2015). Real-Time Topic-Aware Influence Maximization Using Preprocessing. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21786-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21785-7

  • Online ISBN: 978-3-319-21786-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics