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 [1].
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling
Authors: Lin Chen, Forrest Crawford, Amin Karbasi
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our analytical results through an exhaustive set of experiments on both synthetic and real data. and We conduct extensive experiments, on synthetic and real data, to con๏ฌrm the accuracy and the reconstruction performance of RENDER. |
| Researcher Affiliation | Academia | Lin Chen1,2 and Forrest W. Crawford2,3 and Amin Karbasi1,2 1Department of Electrical Engineering, 2Yale Institute for Network Science, 3Department of Biostatistics Yale University, New Haven, CT 06520 EMAIL |
| Pseudocode | Yes | Algorithm 1 RENDER: Alternating inference of GS and ฮธ, Algorithm 2 Simulated-annealing-based optimization, Algorithm 3 Proposal of compatible adjacency matrix |
| Open Source Code | No | The paper does not provide any explicit statements about releasing the source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We simulated a RDS process over the Project 90 graph (Woodhouse et al. 1994) and We also apply RENDER to data from an RDS study of n = 813 drug users in St. Petersburg, Russian Federation. |
| Dataset Splits | No | The paper does not explicitly provide details on training, validation, or test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments, such as GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'FMINSEARCH in MATLAB' but does not provide specific version numbers for MATLAB or any other software dependencies crucial for replication. |
| Experiment Setup | No | While the paper describes parameters for synthetic data generation and theoretical model parameters (e.g., Gamma distribution parameters), it does not provide specific experimental setup details like hyperparameters, learning rates, batch sizes, or optimizer settings for training RENDER. |