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..
Maximizing the Probability of Fixation in the Positional Voter Model
Authors: Petros Petsinis, Andreas Pavlogiannis, Panagiotis Karras
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we propose and experimentally evaluate a number of heuristics for maximizing the fixation probability. We present experimental results for the proposed algorithms and additional heuristics, on 100 randomly-selected strongly connected components of real-life social and community networks (Peixoto 2020), with varying the number of nodes in the range [20, 130]. |
| Researcher Affiliation | Academia | Aarhus University, Denmark EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We present experimental results for the proposed algorithms and additional heuristics, on 100 randomly-selected strongly connected components of real-life social and community networks (Peixoto 2020), with varying the number of nodes in the range [20, 130]. |
| Dataset Splits | No | The paper mentions using 100 randomly-selected strongly connected components for experiments, but does not specify train/validation/test splits or cross-validation details for these networks. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | For each graph we use a budget k corresponding to the 10%, 30% and 50% of the graph s size. We evaluate 7 algorithms that are often used in network analysis. |