Closeness Centrality for Networks with Overlapping Community Structure
Authors: Mateusz Tarkowski, Piotr Szczepański, Talal Rahwan, Tomasz Michalak, Michael Wooldridge
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |