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..
An Attention-Based Graph Neural Network for Heterogeneous Structural Learning
Authors: Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye4132-4139
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions. |
| Researcher Affiliation | Collaboration | 1AI Labs, Didi Chuxing, Beijing, China, 2Peking University, Beijing, China |
| Pseudocode | No | The paper describes the model architecture and equations but does not include an explicit pseudocode block or algorithm figure. |
| Open Source Code | Yes | Available at https://github.com/didi/hetsann |
| Open Datasets | Yes | We collected a movie graph from IMDB site3 and constructed two academic networks from DBLP (Ji et al. 2010) and AMiner (Tang et al. 2008) datasets respectively. ... 3https://www.imdb.com |
| Dataset Splits | Yes | The whole labeled dataset is randomly split into training set, validation set and test set by a ratio of 0.8:0.1:0.1. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions "Adam solver (Kingma and Ba 2015)" but does not provide specific version numbers for this or any other software or libraries used. |
| Experiment Setup | Yes | All variations employ 3-layer Het SANN and each TAL consists of 8 attention heads. The output dimensions of each attention head are consistent to 8. The parameters are optimized via Adam solver (Kingma and Ba 2015) with a learning rate 0.001 for IMDB and 0.005 for other datasets. A regularization weight 0.0005 is applied to all trainable parameters. A dropout rate 0.6 (Srivastava et al. 2014) is implanted between hidden layers to stabilize our model training procedure. For the variant of Het SANN with suffix .V , the weight coefficients β1 =10 3 and β2 =10 5. |