Mining Top-k Popular Datasets via a Deep Generative Model

Abstract

In this paper, we focus on extracting top-k popular datasets used in data mining, machine learning, and artificial intelligence fields. We frame the problem on an attributed citation network with document content and citation relations, then use a semi-supervised multi-label classification formulation and a deep generative model to learn from both content and structure.