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.
Subgraph Federated Learning with Missing Neighbor Generation
Authors: Ke ZHANG, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,297 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,059 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 | Empirical results on four real-world graph datasets with synthesized subgraph federated learning settings demonstrate the effectiveness and efficiency of our proposed techniques. |
| Researcher Affiliation | Academia | Ke Zhang1,4, Carl Yang1 , Xiaoxiao Li2, Lichao Sun3, Siu Ming Yiu4 1Emory University, 2University of British Columbia, 3Lehigh University, 4University of Hong Kong |
| Pseudocode | Yes | Appendix A shows the pseudo code of Fed Sage+. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We synthesize the distributed subgraph system with four widely used real-world graph datasets, i.e., Cora [25], Citeseer [25], Pub Med [22], and MSAcademic [26]. |
| Dataset Splits | Yes | The training-validation-testing ratio is 60%-20%-20% due to limited sizes of local subgraphs. |
| Hardware Specification | Yes | We implement Fed Sage and Fed Sage+ in Python and execute all experiments on a server with 8 NVIDIA Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions implementing in 'Python' but does not specify version numbers for Python or any other libraries/software dependencies like PyTorch, TensorFlow, etc., that would be needed for replication. |
| Experiment Setup | Yes | The number of nodes sampled in each layer of Graph Sage is 5. We use batch size 64 and set training epochs to 50. The training-validation-testing ratio is 60%-20%-20% due to limited sizes of local subgraphs. ... All λs are simply set to 1. Optimization is done with Adam with a learning rate of 0.001. |