Revisiting Graph Autoencoders as Implicit Contrastive Learners

Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs, yet they are often studied in isolation and treated as fundamentally different approaches. In this work, we revisit GAEs through the lens of contrastive learning and show that both structure-based and feature-based GAEs can be conceptualized as implicitly graph contrastive learners. This perspective reveals that many existing GAEs differ primarily in how contrastive views are constructed, rather than in their learning objectives or architectures. Building on this insight, we introduce a unified formulation that highlights contrastive view design as a central and previously less explored dimension in GAEs. In particular, we identify asymmetric contrastive views, arising from mismatches in subgraph views, as an important yet underexplored design axis in prior GAE research. We formalize this insight within a unified framework and conduct systematic experiments on representative graph learning tasks to examine its impact on performance and efficiency. Our results show that interpreting GAEs as implicit contrastive learners offers a clearer understanding of existing models and provides practical guidance for designing effective and scalable graph autoencoders.