D-ViCe: A Dual Variance-Enhanced Contrastive Framework for Graph Representation Learning

Contrastive learning (CL) has become one of the most powerful paradigms in visual self-supervised learning (SSL), but its extension to non-visual modalities such as graphs and CAD data remains underexplored. Although SSL in graphs is closely related to vision, the transfer is non-trivial in practice because graph and CAD representations lack the regular structure and mature augmentation priors of images. For SSL on graph-based CAD representations, we argue that the design of the contrastive targets and learning dynamics can reduce the reliance on carefully tuned, task-specific augmentations. In this work, we propose a novel Dual Variance-Enhanced Contrastive Learning (D-ViCe) framework that employs a dual contrastive mechanism between two target encoders and a shared source encoder. The first target encoder, kept frozen at its random initialization, provides a stationary target for global instance discrimination, while the second, variance-adaptive momentum encoder promotes the refinement of locally invariant representations. Temperature and momentum are central to many CL objectives, yet they are typically fixed or follow predetermined schedules. In contrast, D-ViCe uses training statistics as explicit control signals: the positive-pair probability modulates the contrastive temperature in one branch, and the relative variance of source-teacher similarities governs the target momentum update in the other, thereby balancing global discrimination and local consistency. Experiments on CAD retrieval and graph classification benchmarks show that D-ViCe consistently outperforms state-of-the-art SSL baselines, improving Recall@1 on SolidLetters from $0.515$ to $0.798$, and yields systematic gains when added to existing graph contrastive methods.