Distributed-Order Fractional Graph Operating Network
Authors: Kai Zhao, Xuhao Li, Qiyu Kang, Feng Ji, Qinxu Ding, Yanan Zhao, Wenfei Liang, Wee Peng Tay
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct empirical evaluations across a range of graph learning tasks. The results consistently demonstrate superior performance when compared to traditional continuous GNN models. |
| Researcher Affiliation | Academia | 1Nanyang Technological University, 2Anhui University, 3 Singapore University of Social Sciences |
| Pseudocode | No | The paper describes methods and formulas but does not include structured pseudocode or algorithm blocks (e.g., a clearly labeled 'Algorithm' section). |
| Open Source Code | Yes | The implementation code is available at https://github.com/zknus/NeurIPS-2024-DRAGON. |
| Open Datasets | Yes | For our evaluation on homophilic datasets, we leverage a diverse set of datasets including citation networks (Cora [48], Citeseer [49], Pubmed [50]), tree-structured datasets (Disease and Airport [51]), as well as coauthor and co-purchasing graphs (Coauthor CS [52], Computer and Photo [53]). |
| Dataset Splits | Yes | For the Disease and Airport datasets, we follow the data partitioning and preprocessing procedures as described in [51]. For all other datasets, we adopt random splits for the largest connected component (LCC), in line with the approach detailed in [7]. ...we follow the data splitting strategy described in [66], dividing the data into 50% for training, 25% for validation, and 25% for testing. |
| Hardware Specification | Yes | All experiments are conducted on NVIDIA GeForce RTX 3090 or A5000 GPUs with 24GB of memory. |
| Software Dependencies | No | We generate the data through an open source package Fractional Diff Eq.jl (https://scifracx.org/Fractional Diff Eq.jl/stable/) that is totally driven by Julia and licensed with MIT License. While a package is mentioned, specific version numbers for all key software components are not provided in the text. |
| Experiment Setup | Yes | The hyperparameters employed in Table 4 are detailed in Table 18. ... Dataset Model lr weight decay indrop dropout hidden dim time step size Roman-empire D-CDE 0.005 0.0001 0.4 0.2 80 4 0.2 |