ProfileRank

Finding relevant content and influential users based on information diffusion

Arlei Silva, Sara Guimarães, Wagner Meira, Mohammed Zaki

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

35 Citations (Scopus)

Abstract

Understanding information diffusion processes that take place on the Web, specially in social media, is a fundamental step towards the design of effective information diffusion mechanisms, recommendation systems, and viral marketing/advertising campaigns. Two key concepts in information diffusion are influence and relevance. Influence is the ability to popularize content in an online community. To this end, influentials introduce and propagate relevant content, in the sense that such content satisfies the information needs of a significant portion of this community. In this paper, we study the problem of identifying influential users and relevant content in information diffusion data. We propose ProfileRank, a new information diffusion model based on random walks over a user-content graph. ProfileRank is a PageRank inspired model that exploits the principle that relevant content is created and propagated by influential users and influential users create relevant content. A convenient property of ProfileRank is that it can be adapted to provide personalized recommendations. Experimental results demonstrate that ProfileRank makes accurate recommendations, outperforming baseline techniques. We also illustrate relevant content and influential users discovered using ProfileRank. Our analysis shows that ProfileRank scores are more correlated with content diffusion than with the network structure. We also show that our new modeling is more efficient than PageRank to perform these calculations.

Original languageEnglish
Title of host publicationProceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013
PublisherAssociation for Computing Machinery
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013 - Chicago, IL, United States
Duration: 11 Aug 201314 Aug 2013

Other

Other7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013
CountryUnited States
CityChicago, IL
Period11/8/1314/8/13

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Keywords

  • Influence
  • Information diffusion
  • Relevance

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Silva, A., Guimarães, S., Meira, W., & Zaki, M. (2013). ProfileRank: Finding relevant content and influential users based on information diffusion. In Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013 [2] Association for Computing Machinery. https://doi.org/10.1145/2501025.2501033

ProfileRank : Finding relevant content and influential users based on information diffusion. / Silva, Arlei; Guimarães, Sara; Meira, Wagner; Zaki, Mohammed.

Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013. Association for Computing Machinery, 2013. 2.

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

Silva, A, Guimarães, S, Meira, W & Zaki, M 2013, ProfileRank: Finding relevant content and influential users based on information diffusion. in Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013., 2, Association for Computing Machinery, 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013, Chicago, IL, United States, 11/8/13. https://doi.org/10.1145/2501025.2501033
Silva A, Guimarães S, Meira W, Zaki M. ProfileRank: Finding relevant content and influential users based on information diffusion. In Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013. Association for Computing Machinery. 2013. 2 https://doi.org/10.1145/2501025.2501033
Silva, Arlei ; Guimarães, Sara ; Meira, Wagner ; Zaki, Mohammed. / ProfileRank : Finding relevant content and influential users based on information diffusion. Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013. Association for Computing Machinery, 2013.
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