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..
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. |