Artificial Intelligence Conferences Closeness
Authors: Sébastien Konieczny, Emmanuel Lonca
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we perform an analysis of the main Artificial Intelligence conferences based on principal components analysis and clustering performed on this closeness relation.The data used in the coscinus project (http://www.coscinus. org) are issued from two sources: The DBLP database (https://dblp.uni-trier.de) The CORE ranking (http://www.core.edu.au/conference-portal) |
| Researcher Affiliation | Academia | S ebastien Konieczny and Emmanuel Lonca CRIL CNRS, Universit e d Artois, France {konieczny, lonca}@cril.fr |
| Pseudocode | No | The paper describes the use of algorithms like PCA and k-means clustering but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | This is the aim of the coscinus tool (http://www.coscinus. org), that performs such an analysis from publications data issued from DBLP... This adapted tool can be find here: http://www.coscinus.org/ai. |
| Open Datasets | Yes | The data used in the coscinus project (http://www.coscinus. org) are issued from two sources: The DBLP database (https://dblp.uni-trier.de) The CORE ranking (http://www.core.edu.au/conference-portal) and For replicability, the data used for the experimentations described in this paper are available here: http://coscinus.org/ data. |
| Dataset Splits | No | This paper describes a data analysis project using PCA and clustering on publication data, not a machine learning model that would require traditional train/validation/test dataset splits. Therefore, this information is not applicable and not provided. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory specifications used for running the analysis. |
| Software Dependencies | No | The paper mentions the 'coscinus' tool and the underlying methods (PCA, k-means clustering) but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | Figure 1 shows the 2-dimensional representation obtained by PCA (with MC=3, normalization=min), and the clusters obtained by k-means (for 12 clusters) of the A* conferences in the system (63 conferences). Choosing high values for n allows to focus on mosts prolific authors only, and reduces the noise produced by anomalies. The obtained counting matrix is the basic information we use as closeness measure between publication supports. |