  Learning prerequisite chains is an essential task for efficiently acquiring
knowledge in both known and unknown domains. For example, one may be an expert
in the natural language processing (NLP) domain but want to determine the best
order to learn new concepts in an unfamiliar Computer Vision domain (CV). Both
domains share some common concepts, such as machine learning basics and deep
learning models. In this paper, we propose unsupervised cross-domain concept
prerequisite chain learning using an optimized variational graph autoencoder.
Our model learns to transfer concept prerequisite relations from an
information-rich domain (source domain) to an information-poor domain (target
domain), substantially surpassing other baseline models. Also, we expand an
existing dataset by introducing two new domains: CV and Bioinformatics (BIO).
The annotated data and resources, as well as the code, will be made publicly
available.
