Rethinking and Simplifying Bootstrapped Graph Latents
Wangbin Sun Jintang Li Liang Chen Bingzhe Wu Yatao Bian Zibin Zheng
Graph contrastive learning (GCL) has emerged as a representative paradigm in
graph self-supervised learning, where negative samples are commonly regarded as
the key to preventing model collapse and producing distinguishable
representations. Recent studies have shown that GCL without negative samples
can achieve state-of-the-art performance as well as scalability improvement,
with bootstrapped graph latent (BGRL) as a prominent step forward. However,
BGRL relies on a complex architecture to maintain the ability to scatter
representations, and the underlying mechanisms enabling the success remain
largely unexplored. In this paper, we introduce an instance-level decorrelation
perspective to tackle the aforementioned issue and leverage it as a springboard
to reveal the potential unnecessary model complexity within BGRL. Based on our
findings, we present SGCL, a simple yet effective GCL framework that utilizes
the outputs from two consecutive iterations as positive pairs, eliminating the
negative samples. SGCL only requires a single graph augmentation and a single
graph encoder without additional parameters. Extensive experiments conducted on
various graph benchmarks demonstrate that SGCL can achieve competitive
performance with fewer parameters, lower time and space costs, and significant
convergence speedup.