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Accession Number
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ADA559262
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Title
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Learning to Predict Social Influence in Complex Networks.
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Publication Date
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Mar 2012
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Media Count
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291p
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Personal Author
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K. Saito
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Abstract
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In this project the following five major achievement are made. 1) Two kinds of information diffusion models incorporating asynchronous time delay and a method to select models that better explains the observation, 2) a method to learn and predict opinion share using a variant of voter model, 3) a method to detect changes in opinion share, 4) a method to detect changes in diffusion probability, and 5) a method to learn the strength of opinion. Each of them uses probabilistic models and machine learning techniques to learn the model parameters from the observation. These are important steps to construct basic methods for learning to predict social influence in complex networks. All of them have been published in international conferences and/or international journals. In total there are 17 publications and they are included in the final report.
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Keywords
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Diffusion
Foreign reports
Influential nodes
Information diffusion
Information processing
Japan
Knowledge discovery from network
Learning machines
Models
Network dynamics
Networks
Online communities
Opinion formation
Outbreak detection
Predictions
Probability
Public opinion
Social networks
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Source Agency
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Non Paid ADAS
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NTIS Subject Category
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88B - Information Systems
92C - Social Concerns
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Corporate Author
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Shizuoka Univ. (Japan).
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Document Type
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Technical report
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Title Note
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Final rept. 18 Mar 2010-17 Mar 2012.
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NTIS Issue Number
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1219
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Contract Number
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FA2386-10-1-4053
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