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].
Axiomatization of the PageRank Centrality
Authors: Tomasz Wąs, Oskar Skibski
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose an axiomatization of Page Rank. Specifically, we introduce five simple axioms Foreseeability, Outgoing Homogeneity, Monotonicity, Merging, and Dummy Node and show that Page Rank is the only centrality measure that satisfies all of them. |
| Researcher Affiliation | Academia | Tomasz W as, Oskar Skibski University of Warsaw, Poland EMAIL |
| Pseudocode | No | The paper contains mathematical definitions and proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on mathematical proofs, therefore it does not provide any open-source code for a methodology. |
| Open Datasets | No | The paper is a theoretical work focused on axiomatization and proofs; it does not involve datasets or training. |
| Dataset Splits | No | The paper is a theoretical work and does not include any experimental validation or data splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments or software implementations, so no software dependencies with versions are listed. |
| Experiment Setup | No | The paper is a theoretical work and does not describe any experiments or their setup. |