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Refereed Papers

Track: Data Mining

Paper Title:
Information Flow Modeling based on Diffusion Rate for Prediction and Ranking


  • Xiaodan Song (NEC Laboratories America)
  • Yun Chi (NEC Labs America)
  • Koji Hino (NEC Laboratories America)
  • Belle Tseng (NEC Laboratories America, Inc.)

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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