@inproceedings{
zhao2024distributedorder,
title={Distributed-Order Fractional Graph Operating Network},
author={Kai Zhao and Xuhao Li and Qiyu Kang and Feng Ji and Qinxu Ding and Yanan Zhao and Wenfei Liang and Wee Peng Tay},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kEQFjKqiqM}
}
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 \url{https://github.com/zknus/NeurIPS-2024-DRAGON}.
Distributed-Order Fractional Graph Operating Network
Kai Zhao Xuhao Li Qiyu Kang Feng Ji Qinxu Ding Yanan Zhao Wenfei Liang Wee Peng Tay