Ensemble Multi-Relational Graph Neural Networks
Authors: Yuling Wang, Hao Xu, Yanhua Yu, Mengdi Zhang, Zhenhao Li, Yuji Yang, Wei Wu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |