Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Graphical Hedonic Games of Bounded Treewidth
Authors: Dominik Peters
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 17,854 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,665 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce the notion of a graphical hedonic game and show that, in contrast, on classes of graphical hedonic games whose underlying graphs are of bounded treewidth and degree, such problems become easy. In particular, problems that can be specified through quantification over agents, coalitions, and (connected) partitions can be decided in linear time. The proof is by reduction to monadic second order logic. We also provide faster algorithms in special cases, and show that the extra condition of the degree bound cannot be dropped. |
| Researcher Affiliation | Academia | Dominik Peters Department of Computer Science University of Oxford, UK EMAIL |
| Pseudocode | No | The paper discusses algorithmic approaches and time complexities but does not present any structured pseudocode or algorithm blocks labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any code or provide links to any code repositories related to its methodology. |
| Open Datasets | No | The paper is theoretical and focuses on computational complexity and algorithm design; it does not utilize or refer to any datasets, public or otherwise. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware specifications (e.g., GPU/CPU models) used for running experiments, as no experiments are described. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical and logical constructs; it does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, nor does it provide details about hyperparameters or system-level training settings, as it does not conduct empirical experiments. |