mlsanityData to 14 Oct 2026

Distributed-Order Fractional Graph Operating Network

arXiv 2411.05274 ↗First posted 8 Nov 2024cs.LGcited by 7

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Abstract (arXiv)

We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus. Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON uses a learnable probability distribution over a range of real numbers for the derivative orders. By allowing a flexible and learnable superposition of multiple derivative orders, our framework captures complex graph feature updating dynamics beyond the reach of conventional models. We provide a comprehensive interpretation of our framework's capability to capture intricate dynamics through the lens of a non-Markovian graph random walk with node feature updating driven by an anomalous diffusion process over the graph. Furthermore, to highlight the versatility of the DRAGON framework, we conduct empirical evaluations across a range of graph learning tasks. The results consistently demonstrate superior performance when compared to traditional continuous GNN models. The implementation code is available at urlhttps://github.com/zknus/NeurIPS-2024-DRAGON.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 45 of its 82 references that are in the map. First posted on arXiv 8 Nov 2024.

Builds on

45 of 82 references.

References · 45

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33 more
  1. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
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  20. 2021Beltrami Flow and Neural Diffusion on Graphscited by 84
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  28. 2023Graph Neural Convection-Diffusion with Heterophilycited by 26
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  31. 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDcited by 17
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagationcited by 3
  2. 2025Personalized Subgraph Federated Learning with Sheaf Collaborationcited by 1
  3. 2025Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  4. 2025Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  5. 2026Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitionscited by 0
  6. 2026LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learningcited by 0
  7. 2026Continuous-Time Quantum Walks based Graph Neural Networkcited by 0

Similar, not cited

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  2. 2025Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
  3. 2024Graph Neural Reaction Diffusion Models
  4. 2024Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
  5. 2025Efficient Graph Knowledge Distillation from GNNs to Kolmogorov--Arnold Networks via Self-Attention Dynamic Sampling
  6. 2026Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies
  7. 2026Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models
  8. 2025Diffusion on Graph: Augmentation of Graph Structure for Node Classification