KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
KAUST Grant Number2639
Online Publication Date2019-01-25
Print Publication Date2018-12
Permanent link to this recordhttp://hdl.handle.net/10754/631711
MetadataShow full item record
AbstractFinding popular datasets to work on is essential for data-driven research domains. In this paper, we focus on the problem of extracting top-k popular datasets that have been used in data mining, machine learning, and artificial intelligence fields. We solve this problem on an attributed citation network, which includes node content information (text of published papers) and paper citation relations. By formulating the problem as a semi-supervised multi-label classification one, we develop an efficient deep generative model for learning from both the document content and citation relations. The evaluation on a real-world dataset shows that our proposed model outperforms baseline methods. We then apply the model further to reveal the top-k frequently cited datasets in selected areas and report interesting findings.
CitationAkujuobi U, Sun K, Zhang X (2018) Mining top-k Popular Datasets via a Deep Generative Model. 2018 IEEE International Conference on Big Data (Big Data). Available: http://dx.doi.org/10.1109/BigData.2018.8621957.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2639. This work was performed when Ke Sun was affiliated with KAUST.
Conference/Event name2018 IEEE International Conference on Big Data, Big Data 2018