用分数阶神经扩散模型生成多样图表示，无需负样本即可提升图对比学习效果。
Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
- 基于分数阶微分方程构建可学习编码器，通过调节阶数生成不同视图。
- 在多个同质与异质数据集上达到当前最优性能，无需负样本训练。
- 适用于各类图结构，尤其适合缺乏负样本的场景，如小规模或稀疏图。
图对比学习（GCL）作为无监督图表示学习的新范式，近期取得进展。现有方法分为基于增强和无增强两类：前者依赖复杂数据增强，后者依赖能生成同一输入不同视图的编码器，二者通常需负样本进行训练。本文提出一种基于图神经扩散模型的新型无增强GCL框架。具体地，我们采用由分数阶微分方程（FDE）控制的可学习编码器，每个FDE由微分算子的阶数参数表征。我们证明，调节这些参数可生成能捕获局部或全局信息的多样化视图，用于对比学习。所提模型无需负样本训练，适用于同质与异质数据集。在多种数据集上验证了其有效性，实现当前最优性能。
原文摘要 · Abstract (English)
Graph Contrastive Learning (GCL) has recently made progress as an unsupervised graph representation learning paradigm. GCL approaches can be categorized into augmentation-based and augmentation-free methods. The former relies on complex data augmentations, while the latter depends on encoders that can generate distinct views of the same input. Both approaches may require negative samples for training. In this paper, we introduce a novel augmentation-free GCL framework based on graph neural diffusion models. Specifically, we utilize learnable encoders governed by Fractional Differential Equations (FDE). Each FDE is characterized by an order parameter of the differential operator. We demonstrate that varying these parameters allows us to produce learnable encoders that generate diverse views, capturing either local or global information, for contrastive learning. Our model does not require negative samples for training and is applicable to both homophilic and heterophilic datasets. We demonstrate its effectiveness across various datasets, achieving state-of-the-art performance.
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