| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
In this work, we addressed a problem of efficiently estimating the influence of a node in information diffusion over a social network. Since the information diffusion is a stochastic process, the influence degree of a node is quantified by the expectation, which is usually obtained by very time consuming many runs of simulation. We proposed a framework for predictive simulation based on the leave-N-out cross validation technique that estimates the approximation error of the influence degree of each node without knowing the true influence degree. We experimentally showed that it can serve as a good measure to solve the problem with far fewer runs of simulation ensuring the accuracy. Besides, we applied that framework to computation of node centrality in order to show the broad utility of the proposed resampling-based framework. In addition, we also devised an efficient algorithm that runs an individual information diffusion simulation in parallel on a distributed computing environment.
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