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..
Manipulating Opinion Diffusion in Social Networks
Authors: Robert Bredereck, Edith Elkind
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider several ways of manipulating the majority opinion in a stable outcome, such as bribing agents, adding/deleting links, and changing the order of updates, and investigate the computational complexity of the associated problems, identifying tractable and intractable cases. |
| Researcher Affiliation | Academia | Robert Bredereck University of Oxford Oxford, United Kingdom, TU Berlin, Germany EMAIL Edith Elkind University of Oxford Oxford, United Kingdom EMAIL |
| Pseudocode | No | The paper describes algorithms in prose, but does not contain structured pseudocode or algorithm blocks (e.g., labeled Algorithm figures or sections). |
| Open Source Code | No | The paper does not provide any concrete access (link, explicit statement of release) to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on computational complexity and algorithms. It does not mention using any datasets for training or evaluation in experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and complexity. It does not contain details about an experimental setup, such as hyperparameters or system-level training settings. |