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 [1].
An Axiom System for Feedback Centralities
Authors: Tomasz Wąs, Oskar Skibski
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose an axiom system for four classic feedback centralities: Eigenvector centrality, Katz centrality, Katz prestige and Page Rank. We prove that each of these four centrality measures can be uniquely characterized with a subset of our axioms. |
| Researcher Affiliation | Academia | Tomasz W as , Oskar Skibski University of Warsaw EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |