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..
Relation Structure-Aware Heterogeneous Information Network Embedding
Authors: Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu4456-4463
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification. |
| Researcher Affiliation | Academia | 1Beijing University of Posts and Telecommunications, Beijing, China 2Tsinghua University, Beijing, China |
| Pseudocode | No | The paper describes its methods using prose and mathematical formulas, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | DBLP1, Yelp2 and AMiner3(Tang et al. 2008). 1https://dblp.uni-trier.de 2https://www.yelp.com/dataset/ 3https://www.aminer.cn/citation |
| Dataset Splits | No | The paper states, 'We first randomly separate the original network into training network and testing network, where the training network contains 80% relations to be predicted... and the testing network contains the rest.' and 'we train a logistic classifier with 80% of the labeled nodes and test with the remaining data.' It specifies train and test splits, but does not explicitly mention a separate validation split for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments (e.g., CPU/GPU models, memory, or cloud computing specifications). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | For a fair comparison, we set the embedding dimension d = 100 and the size of negative samples k = 3 for all models. For Deep Walk, HIN2Vec and metapath2vec, we set the number of walks per node w = 10, the walk length l = 100 and the window size τ = 5. For our model RHINE, the margin γ is set to 1. |