Fixation Maximization in the Positional Moran Process
Authors: Joachim Brendborg, Panagiotis Karras, Andreas Pavlogiannis, Asger Ullersted Rasmussen, Josef Tkadlec9304-9312
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation of the new algorithms along with some proposed heuristics corroborates our results. Our data set consists of 110 connected subgraphs of community graphs and social networks of the Stanford Network Analysis Project (Leskovec and Krevl 2014). These subgraphs where chosen randomly, and varied in size between 20-170 nodes. Our evaluation is not aimed to be exhaustive, but rather to outline the practical performance of various heuristics, sometimes in relation to their theoretical guarantees. |
| Researcher Affiliation | Academia | 1Aarhus University, Aabogade 34, Aarhus, Denmark 2Harvard University, 1 Oxford Street, Cambridge, USA |
| Pseudocode | No | The paper describes algorithms and methods verbally and through mathematical equations, but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Our data set consists of 110 connected subgraphs of community graphs and social networks of the Stanford Network Analysis Project (Leskovec and Krevl 2014). |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits. It mentions choosing values of 'k' for budget, not data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of simulations and algorithms but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For each graph G, we have chosen values of k corresponding to 10%, 30% and 50% of its nodes, and have evaluated the above heuristics in their ability to solve FM (G, K) (strong selection) and FM0(G, k) (weak selection). We have not considered other values of δ as evaluating fp(GS, δ) precisely via simulations requires many repetitions and becomes slow. |