Skip to main content
SpringerLink
Log in
Book cover

Australasian Database Conference

ADC 2016: Databases Theory and Applications pp 462–466Cite as

EDMS: A System for Efficient Processing Distance-Aware Influence Maximization

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9877))

Abstract

As a key problem in viral marketing, influence maximization has been widely studied in the literature. It aims to find a set of k users in a social network, which can maximize the influence spread under a certain propagation model. With the proliferation of geo-social networks, location-aware promotion is becoming more and more necessary in real applications. However, the importance of the distance between users and the promoted locations is underestimated in the existing work. For example, when promoting a local store, the owner may prefer to influence more people that are close to the store instead of people that are far away. In this demonstration, we propose EDMS, a centralized system that efficiently processes the distance-aware influence maximization problem. To meet the online requirements, we combine different pruning strategies and the best first search algorithm to significantly reduce the search space. We present a prototype, which provides users with a web interface to issue queries and visualize the search results in real time.

This is a preview of subscription content, 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   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

Notes

  1. 1.

    https://www.djangoproject.com/.

  2. 2.

    https://developers.google.com/maps/.

References

  1. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)

    Google Scholar 

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

    Google Scholar 

  3. Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)

    Google Scholar 

  4. Li, G., Chen, S., Feng, J., Tan, K., Li, W.: Efficient location-aware influence maximization. In: SIGMOD 2014, pp. 87–98 (2014)

    Google Scholar 

  5. Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: SIGMOD, pp. 1539–1554 (2015)

    Google Scholar 

  6. Wang, X., Zhang, Y., Zhang, W., Lin, X.: Distance-aware influence maximization in geo-social network. In: ICDE (2016)

    Google Scholar 

  7. Zhu, W., Peng, W., Chen, L., Zheng, K., Zhou, X.: Modeling user mobility for location promotion in location-based social networks. In: KDD, pp. 1573–1582 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Technology Sydney, Ultimo, Australia

    Xiaoyang Wang & Ying Zhang

  2. The University of New South Wales, Sydney, Australia

    Chen Chen

Authors
  1. Xiaoyang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyang Wang .

Editor information

Editors and Affiliations

  1. Monash University , Clayton, Australia

    Muhammad Aamir Cheema

  2. School of Comp. Science a. Engineer, University of New South Wales School of Comp. Science a. Engineer, Sydney, Australia

    Wenjie Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Wang, X., Chen, C., Zhang, Y. (2016). EDMS: A System for Efficient Processing Distance-Aware Influence Maximization. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46922-5_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46921-8

  • Online ISBN: 978-3-319-46922-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation