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].
Attachment Centrality for Weighted Graphs
Authors: Jadwiga Sosnowska, Oskar Skibski
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | By an axiomatic analysis, we show that the Attachment Centrality is closely related to the Degree Centrality in weighted graphs. Our goal in this paper is to extend Attachment Centrality to node-weighted and edge-weighted graphs. To this end, we ο¬rst propose a new axiomatization of Attachment Centrality for unweighted graphs. Upon this result we will later on build an axiomatization of Attachment Centrality for weighted graphs. |
| Researcher Affiliation | Academia | Jadwiga Sosnowska, Oskar Skibski University of Warsaw, Poland 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 conduct empirical experiments that would require a dataset for training. |
| Dataset Splits | No | The paper does not describe any empirical experiments or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical analysis and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any empirical experiment setup, hyperparameters, or training configurations. |