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Information flow modeling based on diffusion rate for prediction and ranking

Published: 08 May 2007 Publication History

Abstract

Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction . identify where information should flow to, and (2) ranking . identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow.

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cover image ACM Conferences
WWW '07: Proceedings of the 16th international conference on World Wide Web
May 2007
1382 pages
ISBN:9781595936547
DOI:10.1145/1242572
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 May 2007

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Author Tags

  1. collaborative filtering
  2. continuous-time Markov chain
  3. diffusion of innovation
  4. information flow
  5. recommendation
  6. social influence
  7. web ranking

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WWW'07
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WWW'07: 16th International World Wide Web Conference
May 8 - 12, 2007
Alberta, Banff, Canada

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  • (2021)Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformmJournal of Big Data10.1186/s40537-021-00423-z8:1Online publication date: 15-Feb-2021
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