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].
Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems
Authors: Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns
JMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithms were evaluated through experiments on over 20 well-known networks. ... Extensive experimentation on synthetic and real-world networks. |
| Researcher Affiliation | Academia | Abhijin Adiga EMAIL Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904. Chris J. Kuhlman EMAIL Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904. Madhav V. Marathe EMAIL Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative and Department of Computer Science, University of Virginia, Charlottesville, VA 22904. S. S. Ravi EMAIL Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904 and Department of Computer Science, University at Albany State University of New York, Albany, NY 12222. Daniel J. Rosenkrantz EMAIL Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904 and Department of Computer Science, University at Albany State University of New York, Albany, NY 12222. Richard E. Stearns EMAIL Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904 and Department of Computer Science, University at Albany State University of New York, Albany, NY 12222. |
| Pseudocode | Yes | Algorithm 1: Steps of Algorithm Alg-Monotone-Seq Algorithm 2: Steps of Algorithm Approx-QSC Algorithm 3: Greedy heuristic to infer the thresholds. |
| Open Source Code | Yes | 2. The code is available in https://github.com/NSSAC/active_queries_threshold_gds_published_code.git. |
| Open Datasets | Yes | all the mined networks used in our experiments are from the SNAP library (Leskovec and Krevl, 2014). |
| Dataset Splits | No | The paper does not mention specific training/test/validation dataset splits. It discusses evaluating algorithms on entire networks or generating synthetic networks, rather than splitting datasets for model training and evaluation. |
| Hardware Specification | Yes | Compute nodes with 2 x Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz processors with 20 cores per CPU and 386GB memory were used. |
| Software Dependencies | Yes | Implementations were done in Python 2.7 and 3.8. |
| Experiment Setup | Yes | We assigned thresholds in the following manner. Let 0 \u03b8 1 be a real number. For a fixed value of \u03b8, each node v was assigned a threshold value uniformly at random from the interval (d(v) + 2)(1 \u03b8)/2, (d(v) + 2)(1 + \u03b8)/2 . |