Abstract
Link prediction techniques are being increasingly employed to detect covert networks, such as terrorist networks. The challenging problem we have been facing is to improve the performance and accuracy of link prediction methods. We develop an algorithm based on Sequential Bayesian Updating method that combines probabilistic reasoning techniques. This algorithm adopts a recursive way to estimate the statistical confidence of the results a prior and then regenerate observed graphs to make inferences. This novel idea can be efficiently adapt to small datasets in link prediction problems of various engineering applications and science researches. Our experiment with a terrorist network shows significant improvement in terms of prediction accuracy measured by mean average precision. This algorithm has also been integrated into an emergency decision support system (NBCDSS) to provide decision-makers’ auxiliary information.
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Acknowledgments
This work is supported by National Basic Research Program of China (973 program) with Grant No. 2011CB706900, National Natural Science Foundation of China (Grant No. 70971128), Beijing Natural Science Foundation (Grant No. 9102022), and the President Fund of UCAS (Grant No. O95101HY00).
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Jiang, C., Wang, J., Yu, H. (2014). Link Prediction Based on Sequential Bayesian Updating in a Terrorist Network. In: Sun, F., Li, T., Li, H. (eds) Foundations and Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37829-4_27
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DOI: https://doi.org/10.1007/978-3-642-37829-4_27
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