Maximizing the Probability of Fixation in the Positional Voter Model
Authors: Petros Petsinis, Andreas Pavlogiannis, Panagiotis Karras
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {petsinis,pavlogiannis,panos}@cs.au.dk |
| 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. |