Structural Fairness-aware Active Learning for Graph Neural Networks
Authors: Haoyu Han, Xiaorui Liu, Li Ma, MohamadAli Torkamani, Hui Liu, Jiliang Tang, Makoto Yamada
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed method not only improves the GNNs performance but also paves the way for more fair results. |
| Researcher Affiliation | Collaboration | 1Michigan State University 2North Carolina State University 3Shanghai Jiaotong University 4Amazon 5Okinawa Institute of Science and Technology |
| Pseudocode | Yes | Algorithm 1 Algorithm of SCARCE-Structure. |
| Open Source Code | Yes | The code is available at https://anonymous.4open.science/r/SCARCE-D804/. |
| Open Datasets | Yes | We perform experiments utilizing six widely used real-world graph datasets, encompassing three citation datasets, i.e., Cora, Citeseer, and Pubmed (Sen et al., 2008), two co-purchase datasets from Amazon, i.e., Computers and Photo (Shchur et al., 2018), and one OGB dataset, i.e., ogbn-arxiv (Hu et al., 2020). |
| Dataset Splits | No | Due to the lack of validation set in the AL setup, we train the GNN model with fixed 300 epochs and evaluate over the full graph. |
| Hardware Specification | No | The paper does not specify the exact hardware used (e.g., specific GPU or CPU models). |
| Software Dependencies | No | The paper mentions GNN models like GCN, APPNP, GAT, GCNII, and activation functions like ReLU, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Learning Rate Dropout Rate Weight Decay Hidden Size Epochs Activation Function 0.01 0.5 0.0001 16 300 Re LU |