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Network embedding has emerged as a promising research field for networkanalysis. Recently, an approach, named Barlow Twins, has been proposed forself-supervised learning in computer vision by applying theredundancy-reduction principle to the embedding vectors corresponding to twodistorted versions of the image samples. Motivated by this, we propose BarlowGraph Auto-Encoder, a simple yet effective architecture for learning networkembedding. It aims to maximize the similarity between the embedding vectors ofimmediate and larger neighborhoods of a node, while minimizing the redundancybetween the components of these projections. In addition, we also present thevariation counterpart named as Barlow Variational Graph Auto-Encoder. Ourapproach yields promising results for inductive link prediction and is also onpar with state of the art for clustering and downstream node classification, asdemonstrated by extensive comparisons with several well-known techniques onthree benchmark citation datasets.

Barlow Graph Auto-Encoder for Unsupervised Network Embedding