# Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

arXiv: https://arxiv.org/abs/2109.08722 · HTML: https://mlsanity.com/paper/2109.08722/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [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)
- [SIGN: Scalable Inception Graph Neural Networks](https://mlsanity.com/paper/2004.11198/) (2020)
- [Scaling Graph Neural Networks with Approximate PageRank](https://mlsanity.com/paper/2007.01570/) (2020)
- [Unsupervised Statistical Machine Translation](https://mlsanity.com/paper/1809.01272/) (2018)
- [Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1606.01614/) (2016)
- [Multinomial Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/1802.05694/) (2018)
- [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](https://mlsanity.com/paper/1811.12181/) (2018)
- [Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders](https://mlsanity.com/paper/2105.03505/) (2021)
- [Detecting Bias in Transfer Learning Approaches for Text Classification](https://mlsanity.com/paper/2102.02114/) (2021)

## Built on by (most cited first, 3 of 3)

- [Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective](https://mlsanity.com/paper/2410.17600/) (2024)
- [EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts](https://mlsanity.com/paper/2204.06604/) (2022)
- [CLICKER: A Computational LInguistics Classification Scheme for Educational Resources](https://mlsanity.com/paper/2112.08578/) (2021)

## Abstract (arXiv)

Prerequisite chain learning helps people acquire new knowledge efficiently. While people may quickly determine learning paths over concepts in a domain, finding such paths in other domains can be challenging. We introduce Domain-Adversarial Variational Graph Autoencoders (DAVGAE) to solve this cross-domain prerequisite chain learning task efficiently. Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator. The VGAE is trained to predict concept relations through link prediction, while the domain discriminator takes both source and target domain data as input and is trained to predict domain labels. Most importantly, this method only needs simple homogeneous graphs as input, compared with the current state-of-the-art model. We evaluate our model on the LectureBankCD dataset, and results show that our model outperforms recent graph-based benchmarks while using only 1/10 of graph scale and 1/3 computation time.