Querying and tracking influencers in social streams

Karthik Subbian, Charu C. Aggarwal, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Influence analysis is an important problem in social network analysis due to its impact on viral marketing and targeted advertisements. Most of the existing influence analysis methods determine the influencers in a static network with an influence propagation model based on pre-defined edge propagation probabilities. However, none of these models can be queried to find influencers in both context and time-sensitive fashion from a streaming social data. In this paper, we propose an approach to maintain real-time influence scores of users in a social stream using a topic and time-sensitive approach, while the network and topic is constantly evolving over time. We show that our approach is efficient in terms of online maintenance and effective in terms various types of real-time context- and time-sensitive queries. We evaluate our results on both social and collaborative network data sets.

Original languageEnglish
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages493-502
Number of pages10
ISBN (Print)9781450337168
DOIs
Publication statusPublished - 8 Feb 2016
Externally publishedYes
Event9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States
Duration: 22 Feb 201625 Feb 2016

Other

Other9th ACM International Conference on Web Search and Data Mining, WSDM 2016
CountryUnited States
CitySan Francisco
Period22/2/1625/2/16

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ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications

Cite this

Subbian, K., Aggarwal, C. C., & Srivastava, J. (2016). Querying and tracking influencers in social streams. In WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining (pp. 493-502). Association for Computing Machinery, Inc. https://doi.org/10.1145/2835776.2835788