Abstract
In this paper, we proposes a method to understand how research fields evolve through the statistical analysis of research publications and the number of new authors in a particular field. Using a Dynamic Bayesian Network, together with the proposed transitive closure property, a more accurate model can be constructed to better represent the temporal features of how a research field evolves. Experiments on the KDD related conferences indicate that the proposed method can discover interesting models effectively and help researchers to get a better insight looking at unfamiliar research areas.
Supported by the National Natural Science Foundation of P.R.China (60402010) and Zheiang Provincial Natural Science Foundation of P.R.China (Y105250).
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Wang, J., Xu, C., Li, G., Dai, Z., Luo, G. (2007). Understanding Research Field Evolving and Trend with Dynamic Bayesian Networks . In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_32
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DOI: https://doi.org/10.1007/978-3-540-71701-0_32
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