Relation Structure-Aware Heterogeneous Information Network Embedding
Authors: Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu4456-4463
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |