View a PDF of the paper titled Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks, by Yanan Zhao, Feng Ji, Jingyang Dai, Jiaze Ma, Keyue Jiang, Kai Zhao, and Wee Peng Tay.
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Abstract: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 $alpha in (0,1]$, our encoders produce a continuous spectrum of views: small $alpha$ yields localized features, while large $alpha$ induces broader, global aggregation. We treat $alpha$ 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.
Submission History
From: Yanan Zhao [view email]
[v1] Sun, 9 Nov 2025 04:01:46 UTC (522 KB)
[v2] Mon, 9 Mar 2026 14:21:31 UTC (522 KB)
[v3] Wed, 18 Mar 2026 15:12:05 UTC (522 KB)
### Understanding Graph Contrastive Learning
Graph contrastive learning (GCL) has emerged as a pivotal technique in machine learning, focusing on how to derive effective representations from graph data. What differentiates GCL from traditional approaches is its reliance on contrasting multiple views of a graph. This inherently dual perspective—where both local and global features are emphasized—enables the generation of richer node and graph representations. However, many existing strategies utilize fixed, handcrafted views, which can stymie their ability to adapt to varied structural patterns found in different datasets.
### The Breakthrough of Fractional-order Neural Diffusion Networks
The recent exploration into fractional-order dynamics marks a significant advance in GCL methodologies. Fractional-order Neural Diffusion Networks leverage this concept by modifying how information spreads through a graph. Specifically, by adjusting the fractional derivative order, researchers can tailor features to capture both localized details and overarching global trends effectively.
For instance, a smaller value of the fractional order (denoted as α) leads to more localized representations, which can be particularly useful in identifying specific nodes or edges that carry significant importance. In contrast, a larger α value fosters broader aggregation, allowing the model to capture wider structural nuances across the graph.
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### Learnable Parameters for Enhanced Adaptability
One of the innovative aspects of the proposed framework is the treatment of α as a learnable parameter. This adaptability allows the model not just to fix its perspective but rather to adjust dynamically based on the characteristics of the data it is processing. By learning to vary the diffusion scales contextually, the model can automatically uncover important views that conventional approaches might overlook. This self-optimizing mechanism is critical for advancing the effectiveness of GCL in real-world applications.
### Advantages of an Augmentation-Free Approach
A notable feature of this framework is its augmentation-free characteristic. Traditional GCL techniques often rely on data augmentation strategies to generate additional training examples, which can sometimes lead to overfitting or introduce unwanted bias. By utilizing continuous dynamics grounded in fractional-order mathematics, the method sidesteps these issues while still providing diverse, complementary representations. Such an approach ensures that the embeddings generated are both robust and reflective of the underlying graph structure without the pitfalls associated with manual augmentations.
### Performance Benchmarks
Empirical evaluations illustrate the superiority of the proposed method over state-of-the-art GCL baselines. In extensive experiments on standard benchmarks, the framework has demonstrated its capacity to yield more expressive and reliable embeddings. This advancement not only highlights the method’s efficiency but also underscores its practical utility across various graph-related tasks, from recommendation systems to social network analysis.
### The Future of Graph Learning
As GCL continues to evolve and integrate with innovative techniques like those presented in this study, the future looks bright for graph learning applications. With the ability to adapt dynamically, these frameworks can offer deeper insights into complex graph structures, paving the way for breakthroughs in numerous fields ranging from computational biology to social sciences.
By delving into adaptive learning strategies and leveraging mathematical innovations such as fractional dynamics, researchers can enhance the interpretative power of graph representations and respond more effectively to the growing demands of data-centric industries.
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