FairWire: Fair Graph Generation
Authors: Oyku Kose, Yanning Shen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world networks validate that the proposed tools herein deliver effective structural bias mitigation for both real and synthetic graphs. |
| Researcher Affiliation | Academia | O. Deniz Kose Department of Electrical Engineering and Computer Science University of California Irvine Irvine, CA, USA okose@uci.edu Yanning Shen Department of Electrical Engineering and Computer Science University of California Irvine Irvine, CA, USA yannings@uci.edu |
| Pseudocode | No | The paper describes algorithms and processes verbally and with mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes to reproduce all the results in Section 6 are provided in the supplementary material to this submission. |
| Open Datasets | Yes | In the experiments, four attributed networks are employed, namely Cora, Citeseer, Amazon Photo and Amazon Computer for link prediction. Cora and Citeseer are widely utilized citation networks, where the articles are nodes and the network topology depicts the citation relationships between these articles (54). Amazon Photo and Amazon Computer are product co-purchase networks, where the nodes are the products and the links are created if two products are often bought together (55). In addition to link prediction, we also evaluate the synthetic graphs on node classification, where the German credit (56) and Pokec-n (9) graphs are employed. |
| Dataset Splits | Yes | For training, 80% of the edges are used, where the remaining edges are split equally into two for the validation and test sets. |
| Hardware Specification | Yes | Experiments are carried over on 4 NVIDIA RTX A4000 GPUs. |
| Software Dependencies | No | The paper mentions using the “Adam optimizer (65)” and “Glorot initialization (64)”, but it does not specify software dependencies like programming languages, libraries, or frameworks with their version numbers. |
| Experiment Setup | Yes | The learning rate, the dimension of hidden representations, and the dropout rate are selected via grid search for the proposed scheme and all baselines, where the value leading to the best validation set performance is selected. For learning rate the, the dimension of hidden representations, and the dropout rate, the corresponding hyperparameter spaces are {1e 1, 1e 2, 3e 3, 1e 3}, {32, 128, 512}, and {0.0, 0.1, 0.2}, respectively. |