Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
Authors: Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |