document: Information flow modeling based on diffusion rate for prediction and ranking 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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