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..
Ensemble Multi-Relational Graph Neural Networks
Authors: Yuling Wang, Hao Xu, Yanhua Yu, Mengdi Zhang, Zhenhao Li, Yuji Yang, Wei Wu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on four benchmark datasets well demonstrate the effectiveness of the proposed model. |
| Researcher Affiliation | Collaboration | Yuling Wang1,2 , Hao Xu2 , Yanhua Yu1 , Mengdi Zhang2 , Zhenhao Li1 , Yuji Yang2 and Wei Wu 2 1Beijing University of Posts and Telecommunications 2Meituan |
| Pseudocode | Yes | The pseudocode of En MP layer is shown in appendix A. Algorithm 1 Relational Coefficients Learning |
| Open Source Code | Yes | Code and appendix are at https://github.com/tuzibupt/EMR. |
| Open Datasets | Yes | The following four real-world heterogeneous datasets in various fields are utilized and can be divided into two categories: i) the node type and edge type are both heterogeneous (DBLP [Fu et al., 2020], ACM [Lv et al., 2021]). ii) the node type is homogeneous but the edge type is heterogeneous (MUTAG [Schlichtkrull et al., 2018], BGS [Schlichtkrull et al., 2018]). |
| Dataset Splits | No | We conduct 10 runs on all datasets with the fixed training/validation/test split for all experiments. The paper states a fixed split but does not provide specific percentages or sample counts for the validation set, which is necessary for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | We implement EMR-GNN based on Pytorch. The paper mentions PyTorch but does not specify its version number or any other software dependencies with their versions, which is required for reproducibility. |
| Experiment Setup | Yes | For f (X; W) and gθ( ), we choose one layer MLP for DBLP and ACM, and linear layers for MUTAG and BGS. We conduct 10 runs on all datasets with the fixed training/validation/test split for all experiments. More implementation details can be seen in appendix B.3. |