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..
Uncovering the Structural Fairness in Graph Contrastive Learning
Authors: Ruijia Wang, Xiao Wang, Chuan Shi, Le Song
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various benchmarks and evaluation protocols validate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | Ruijia Wang1, Xiao Wang1 , Chuan Shi1 , Le Song2 1Beijing University of Posts and Telecommunications 2 Bio Map and MBZUAI EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methods in text and equations but does not include a dedicated pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not contain an explicit statement or link providing access to the source code for the described methodology. |
| Open Datasets | Yes | Specifically, we choose two categories of datasets: 1) citation networks including Cora [17] and Citeseer [17], 2) social networks Photo [23] and Computer [23] from Amazon. |
| Dataset Splits | No | The paper mentions 'early stopping based on the training loss', but does not specify an explicit validation dataset split percentage or count. |
| Hardware Specification | No | The paper mentions running experiments but does not specify any particular hardware components such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions various models and frameworks (e.g., GCN, DGI, Graph CL) but does not provide specific version numbers for software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | We investigate the impact of threshold ΞΆ used to split tail nodes and head nodes on classification performance. Figure 4 (a) shows the test Micro-F1 w.r.t. different ΞΆ on Cora dataset. We perform sensitivity analysis on feature drop rate dfdr and edge drop rate dedr which control the generation of graph augmentations. |