# Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

arXiv: https://arxiv.org/abs/2105.03505 · HTML: https://mlsanity.com/paper/2105.03505/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Unsupervised Statistical Machine Translation](https://mlsanity.com/paper/1809.01272/) (2018)
- [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](https://mlsanity.com/paper/1811.12181/) (2018)
- [R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning](https://mlsanity.com/paper/2004.10610/) (2020)

## Built on by (most cited first, 11 of 11)

- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective](https://mlsanity.com/paper/2410.17600/) (2024)
- [Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://mlsanity.com/paper/2307.06576/) (2023)
- [Integrating UMLS Knowledge into Large Language Models for Medical Question Answering](https://mlsanity.com/paper/2310.02778/) (2023)
- [Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education](https://mlsanity.com/paper/2407.10794/) (2024)
- [NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods](https://mlsanity.com/paper/2302.06132/) (2023)
- [Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains](https://mlsanity.com/paper/2109.08722/) (2021)
- [Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education](https://mlsanity.com/paper/2402.14293/) (2024)
- [HiPool: Modeling Long Documents Using Graph Neural Networks](https://mlsanity.com/paper/2305.03319/) (2023)
- [CLICKER: A Computational LInguistics Classification Scheme for Educational Resources](https://mlsanity.com/paper/2112.08578/) (2021)
- [A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation](https://mlsanity.com/paper/2201.02312/) (2022)

## Abstract (arXiv)

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.