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..
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
Authors: Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments with synthetic and realworld data, which demonstrate the practical value of our theoretical and algorithmic contributions. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University 2Department of Statistics and Data Science Institute, University of Chicago 3Department of Economics, Stanford University 4Department of Management Science & Engineering, Stanford University. |
| Pseudocode | Yes | Algorithm 1 Our implementation of the iterative proportional fitting procedure. |
| Open Source Code | Yes | Our code is available at https://github.com/ snap-stanford/ipf-network-inference. |
| Open Datasets | Yes | Citibike data (Citi Bike, 2023) and Safe Graph mobility data (Dewey, 2023) are available online. |
| Dataset Splits | No | The paper does not provide explicit training, validation, and test dataset splits with proportions or sample counts. It describes experiments on entire datasets (synthetic or real-world over specific time periods) rather than predefined splits. |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU, CPU models, cloud computing resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python s statsmodels package' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We sample the row scaling factors eu Rm and column scaling factors e v Rn from Uniform(0, 4). We sample X Rm n from Uniform(0, 1). For a given sparsity level r [0, 1), we randomly select r mn entries from X (without replacement) and set them to 0. For each sparsity rate in r {0, 0.05, , 0.9}, we run 1000 random trials. |