PageRank for Edges: Axiomatic Characterization
Authors: Natalia Kucharczuk, Tomasz Wąs, Oskar Skibski5108-5115
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | With this paper, we initiate the discussion on the axiomatic properties of edge centrality measures. We do it by proposing an axiomatic characterization of Edge Page Rank. Our characterization is the first characterization of any edge centrality measure in the literature. We will prove that F satisfies Edge Page Rank recursive equation (Equation (1)) with decay factor a F for every graph G = (V, E, φ, b) and edge e : (v, w) E. Since this equation uniquely define Edge Page Rank, it will imply that F is indeed Edge Page Rank. |
| Researcher Affiliation | Academia | Institute of Informatics, University of Warsaw, Poland nk406686@students.mimuw.edu.pl, t.was@mimuw.edu.pl, o.skibski@mimuw.edu.pl |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | This paper is theoretical and does not involve the use of datasets for training or evaluation. The example provided includes specific values but these do not constitute a publicly available dataset for experimental purposes. |
| Dataset Splits | No | This paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments that would require specific hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe implementation details or experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup, hyperparameters, or training settings. |