Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 |