Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data
Authors: Sheng Huang, Lele Fu, Tianchi Liao, Bowen Deng, Chuanfu Zhang, Chuan Chen
IJCAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 16,034 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,007 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets demonstrate the sufficient motivation and effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Sheng Huang , Lele Fu , Tianchi Liao , Bowen Deng , Chuanfu Zhang and Chuan Chen Sun Yat-sen University, Guangzhou, China EMAIL, EMAIL |
| Pseudocode | No | The paper states: "The complete algorithm is shown in Appendix." However, the provided text does not include an appendix with the algorithm or pseudocode. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The evaluations are performed on two real-world datasets, including Twitch and Facebook100. |
| Dataset Splits | Yes | 10% of the nodes of each domain s graph data in Facebook100 are sampled as the training data to make Facebook100-lite. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations in the main text. It refers to an appendix for the complete algorithm, but the appendix is not provided. |