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 [1].
GAEN: Graph Attention Evolving Networks
Authors: Min Shi, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang, Jianxun Liu
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks. |
| Researcher Affiliation | Academia | Min Shi1 , Yu Huang1 , Xingquan Zhu1 , Yufei Tang1 , Yuan Zhuang2 and Jianxun Liu3 1Dept. of Computer & Elec. Engineering and Computer Science, Florida Atlantic University, USA 2 State Key Lab of Info. Eng. in Surveying, Mapping and Remote Sensing, Wuhan University, China 3School of Computer Science and Engineering, Hunan University of Science and Technology, China EMAIL, EMAIL |
| Pseudocode | Yes | The detailed training procedure of GAEN is summarized in Algorithm 1. |
| Open Source Code | Yes | For detailed parameter settings, please refer to the Git Hub link2. 2https://github.com/codeshareabc/GAEN |
| Open Datasets | Yes | We adopt four temporal networks Enron [Klimt and Yang, 2004], UIC [Panzarasa et al., 2009], Primary School [Stehl e et al., 2011] and DBLP1 summarized in Table 1. 1https://dblp.uni-trier.de |
| Dataset Splits | Yes | For link prediction, 20% of the links are used as validation to fine-tune the hyper-parameters, and the remaining are split as 25% and 75% for training and test. For node classification, 20% of nodes are used for validation. Then, 30% and 70% of the remaining nodes are respectively used for training. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU model, CPU model, memory) used for running the experiments. |
| Software Dependencies | No | The paper discusses methods and models (e.g., GRU, GAT, GCN) but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | To train the model, the number of attention heads is set as 8, the hidden dimension in GRU networks is set as 128 and the learning rate is set as 1e-4. |