Mining top-k Popular Datasets via a Deep Generative Model

Uchenna Akujuobi, Ke Sun, Xiangliang Zhang

Abstract: Finding 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.