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..
SlotGAT: Slot-based Message Passing for Heterogeneous Graphs
Authors: Ziang Zhou, Jieming Shi, Renchi Yang, Yuanhang Zou, Qing Li
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superiority of Slot GAT is evaluated against 13 baselines on 6 datasets for node classification and link prediction. |
| Researcher Affiliation | Collaboration | 1Department of Computing, The Hong Kong Polytechnique Univesity 2Department of Computer Science, Hong Kong Baptist University 3Tencent. |
| Pseudocode | Yes | Algorithm 1 shows the pseudo code of Slot GAT. |
| Open Source Code | Yes | Our code is at https://github.com/ scottjiao/Slot GAT_ICML23/. |
| Open Datasets | Yes | Table 1 reports the statistics of benchmark datasets widely used in (Lv et al., 2021; Wang et al., 2019d; Zhao et al., 2022; Zhang et al., 2019; Yun et al., 2019; Yang et al., 2022). The descriptions of all datasets are in Appendix A.1. For all of the benchmark datasets, one could access them in online platform HGB1. Footnote 1 refers to: https://www.biendata.xyz/hgb/ |
| Dataset Splits | Yes | For node classification, following (Lv et al., 2021), we split labeled training set into training and validation with ratio 80% : 20%, while the testing data are fixed with detailed numbers in Appendix A.2 Table 12. For link prediction, we adopt ratio 81% : 9% : 10% to divide the edges into training, validation, and testing. |
| Hardware Specification | Yes | All experiments are conducted on a machine powered by an Intel(R) Xeon(R) E5-2603 v4 @ 1.70GHz CPU, 131GB RAM, and a Nvidia Geforce 3090 Cards with Cuda version 11.3. |
| Software Dependencies | Yes | All experiments are conducted on a machine powered by an Intel(R) Xeon(R) E5-2603 v4 @ 1.70GHz CPU, 131GB RAM, and a Nvidia Geforce 3090 Cards with Cuda version 11.3. |
| Experiment Setup | Yes | Hyper-parameter Search Space. We search learning rate within {1, 5} {1e 5, 1e 4, 1e 3, 1e 2}, weight decay rate within {1, 5} {1e 5, 1e 4, 1e 3}, dropout rate for features within {0.2, 0.5, 0.8, 0.9}, dropout rate for connections within {0, 0.2, 0.5, 0.8, 0.9}, and number of hidden layers L within {2, 3, 4, 5, 6}. We use the same dimension of hidden embeddings across all layers dl within {32, 64, 128}. We search the number of epochs within the range of {40, 300, 1000} with early stopping patience 40, and dimension ds of slot attention vector within the range of {3, 8, 32, 64}. Following (Lv et al., 2021), for input feature type, we use feat = 0 to denote the use of all given features, feat = 1 to denote using only target node features (zero vector for others), and feat = 2 to denote all nodes with one-hot features. For node classification, we use feat 1 and set the number of attention heads K to be 8. For link prediction, we use feat 2 and set K to be 2. |