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..
On the Synergy of Network Science and Artificial Intelligence
Authors: Decebal Constantin Mocanu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At the same time, the practical applicability of these concepts was not let behind, and we have demonstrated their validity in the context of real-world settings, e.g. image/video quality assessment in communication networks [Mocanu et al., 2014b; 2015b; 2015c], computer vision [Mocanu et al., 2014c; 2015a; 2016a]. |
| Researcher Affiliation | Academia | Decebal Constantin Mocanu Eindhoven University of Technology, The Netherlands |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to repositories. |
| Open Datasets | No | The paper mentions 'real-world settings' and cites other papers (e.g., 'image/video quality assessment in communication networks [Mocanu et al., 2014b; 2015b; 2015c]'), but it does not provide concrete access information (link, DOI, repository, or direct citation within this paper) for a publicly available dataset used for its experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |