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..
Axioms for Distance-Based Centralities
Authors: Oskar Skibski, Jadwiga Sosnowska
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We axiomatize the class of distance-based centralities and study what conditions are imposed by the axioms proposed in the literature. Building upon our analysis, we propose the class of additive distance-based centralities and pin-point properties which combined with the axiomatic characterization of the whole class uniquely characterize a number of centralities from the literature. |
| Researcher Affiliation | Academia | Oskar Skibski, Jadwiga Sosnowska University of Warsaw, Poland |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focused on axiomatization and does not use or reference datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not describe any training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about experimental setup, hyperparameters, or system-level training settings. |