图对比学习在小数据上不如随机初始化模型，大数据才显优势。
Graph Contrastive Learning versus Untrained Baselines: The Role of Dataset Size
- 用未训练的GNN和手工特征做对比，发现小数据集上表现不输图对比学习
- 在ogbg-molhiv数据集上，超几千张图后图对比学习才开始领先并趋于饱和
- 适合关注数据规模影响的研究者，尤其分子药物领域应用者
图对比学习（GCL）已成为图自监督学习的主流方法，在标准数据集上表现优异，并被广泛应用于基因组学与药物发现等领域。我们提出一个基础问题：GCL是否真的优于未训练基线？研究发现，其优势强烈依赖于数据集规模与任务难度。在标准数据集上，未训练的图神经网络、多层感知机甚至手工统计特征均可与或超过GCL表现。在大型分子数据集ogbg-molhiv上，观察到交叉点：小规模时GCL落后，但超过数千张图后逐渐领先，且最终性能趋于平稳。在合成数据集上，GCL准确率约与图数量的对数成正比，与未训练GNN的性能差距随任务复杂度变化。未来需重视基准测试中数据规模的作用，并设计能避免性能饱和的GCL算法。
原文摘要 · Abstract (English)
Graph Contrastive Learning (GCL) has emerged as a leading paradigm for self-supervised learning on graphs, with strong performance reported on standardized datasets and growing applications ranging from genomics to drug discovery. We ask a basic question: does GCL actually outperform untrained baselines? We find that GCL's advantage depends strongly on dataset size and task difficulty. On standard datasets, untrained Graph Neural Networks (GNNs), simple multilayer perceptrons, and even handcrafted statistics can rival or exceed GCL. On the large molecular dataset ogbg-molhiv, we observe a crossover: GCL lags at small scales but pulls ahead beyond a few thousand graphs, though this gain eventually plateaus. On synthetic datasets, GCL accuracy approximately scales with the logarithm of the number of graphs and its performance gap (compared with untrained GNNs) varies with respect to task complexity. Moving forward, it is crucial to identify the role of dataset size in benchmarks and applications, as well as to design GCL algorithms that avoid performance plateaus.
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