Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains
This work addresses the challenge of efficiently finding learning paths across different domains, which is incremental as it builds on existing graph-based methods with improvements in efficiency.
The paper tackles the problem of cross-domain prerequisite chain learning by introducing Domain-Adversarial Variational Graph Autoencoders (DAVGAE), which outperforms recent graph-based benchmarks using only 1/10 of the graph scale and 1/3 of the computation time on the LectureBankCD dataset.
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.