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..
Invasion Dynamics in the Biased Voter Process
Authors: Loke Durocher, Panagiotis Karras, Andreas Pavlogiannis, Josef Tkadlec
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation of some proposed heuristics corroborates our results. |
| Researcher Affiliation | Academia | Aarhus University, Aabogade 34, Aarhus, Denmark Harvard University, 1 Oxford Street, Cambridge, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate all methods on four networks from the Netzschleuder database [Peixoto, 2020], chosen arbitrarily. |
| Dataset Splits | No | The paper mentions selecting a budget k for the initial seed set (k = 5% of nodes), but does not provide specific train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In each case, we choose a budget k equal to 5% of the nodes of the graph. |