  Network embedding has emerged as a promising research field for network
analysis. Recently, an approach, named Barlow Twins, has been proposed for
self-supervised learning in computer vision by applying the
redundancy-reduction principle to the embedding vectors corresponding to two
distorted versions of the image samples. Motivated by this, we propose Barlow
Graph Auto-Encoder, a simple yet effective architecture for learning network
embedding. It aims to maximize the similarity between the embedding vectors of
immediate and larger neighborhoods of a node, while minimizing the redundancy
between the components of these projections. In addition, we also present the
variation counterpart named as Barlow Variational Graph Auto-Encoder. Our
approach yields promising results for inductive link prediction and is also on
par with state of the art for clustering and downstream node classification, as
demonstrated by extensive comparisons with several well-known techniques on
three benchmark citation datasets.
