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..
Closeness Centrality for Networks with Overlapping Community Structure
Authors: Mateusz Tarkowski, Piotr Szczepański, Talal Rahwan, Tomasz Michalak, Michael Wooldridge
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate this measure and our algorithm that computes it by analysing the Warsaw public transportation network. |
| Researcher Affiliation | Academia | 2Department of Computer Science, University of Oxford, United Kingdom 2 Warsaw University of Technology, Poland 3Masdar Institute of Science and Technology, United Arab Emirates 4Institute of Informatics, University of Warsaw, Poland |
| Pseudocode | Yes | Algorithm 1: Precomputations for Configuration Semivalue Closeness. input : Graph G = (V, E, ω), Closeness function f : R R, Overlapping Community Structure CS, Probability distribution functions β : 0, 1, . . . |V | 1 R, 0 j |CS| 1αj : 0, 1, . . . |Qj| 1 R output: Configuration Semivalue |
| Open Source Code | Yes | 7The data, experiment results and programs can be downloaded from https://github.com/szczep/gtna. |
| Open Datasets | Yes | We conducted an experiment on a You Tube social network with ground-truth communities (Mislove et al. 2007)... Available at https://snap.stanford.edu/data/com-Youtube.html |
| Dataset Splits | No | The paper describes using the Warsaw public transportation network and a YouTube subnetwork, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper states that algorithms were implemented in Java but does not provide any specific hardware details such as GPU/CPU models or memory amounts used for running experiments. |
| Software Dependencies | No | The paper states that the algorithms were implemented in 'Java' but does not provide specific version numbers for Java or any other software dependencies or libraries. |
| Experiment Setup | No | The paper describes the characteristics of the analyzed networks (e.g., edge-weights defined as average travel times) but does not specify experimental setup details such as hyperparameters or training configurations typically found in machine learning experiments, as the paper focuses on a centrality measure computation rather than model training. |