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 | Conference PDF | Archive PDF | Plain Text | 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. |