Prediction of Rising Venues in Citation Networks
Muhammad Azam Zia, Zhongbao Zhang, Guangda Li, Haseeb Ahmad, and Sen Su
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
10 Xitucheng Rd., Haidian District, Beijing 100876, China
Prediction of rising stars has become a core issue in data mining and social networks. Prediction of rising venues could unveil rapidly emerging research venues in citation network. The aim of this research is to predict the rising venues. First, we presented five effective prediction features along with their mathematical formulations for extracting rising venues. The underlying features are composed by incorporating the citation count, publications, cited to and cited by information at venue level. For prediction purpose, we employ four machine learning algorithms including Bayesian Network, Support Vector Machine, Multilayer Perceptron and Random Forest. Experimental results demonstrate that proposed features set are effective for rising venues prediction. Our empirical analysis spotlights the rising venues that demonstrate the continuous improvement over time and finally become the leading scientific venues.
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