用可学习的分数阶扩散，自动生成多尺度图表示。
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
- 通过可调分数阶导数生成连续多视图，无需人工设计
- 在多个基准上优于现有最先进方法，提升表征能力
- 适合需要自适应多尺度图分析的研究者
图对比学习（GCL）通过对比同一图的多个视图来学习节点和图的表示。现有方法通常依赖固定的、手工设计的视图——通常是局部和全局视角，限制了捕捉多尺度结构模式的能力。本文提出一种无增强的多视图GCL框架，基于分数阶连续动力学。通过调整分数阶导数阶数 α∈(0,1]，编码器可产生连续的视图谱：小α生成局部特征，大α则诱导更广域的全局聚合。我们将α作为可学习参数，使模型能根据数据自适应调整扩散尺度，自动发现信息丰富的视图。该原理性方法在不依赖人工增强的情况下生成多样且互补的表示。在标准基准上的大量实验表明，该方法能生成更鲁棒、更具表达力的嵌入，并超越现有最先进GCL基线。
原文摘要 · Abstract (English)
Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically rely on fixed, handcrafted views-usually a local and a global perspective, which limits their ability to capture multi-scale structural patterns. We present an augmentation-free, multi-view GCL framework grounded in fractional-order continuous dynamics. By varying the fractional derivative order $α\in (0,1]$, our encoders produce a continuous spectrum of views: small $α$ yields localized features, while large $α$ induces broader, global aggregation. We treat $α$ as a learnable parameter so the model can adapt diffusion scales to the data and automatically discover informative views. This principled approach generates diverse, complementary representations without manual augmentations. Extensive experiments on standard benchmarks demonstrate that our method produces more robust and expressive embeddings and outperforms state-of-the-art GCL baselines.
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