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 [1].
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. |