Understanding research field evolving and trend with dynamic Bayesian networks

Authors:
Jinlong Wang;Congfu Xu;Gang Li;Zhenwen Dai;Guojing Luo
Affiliations:
Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;School of Engineering and Information Technology, Deakin University, VIC, Australia;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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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.