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..
Group Activity Selection on Social Networks
Authors: Ayumi Igarashi, Dominik Peters, Edith Elkind
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that if multiple groups can simultaneously engage in the same activity, finding a stable outcome is easy as long as the network is acyclic. In contrast, if each activity can be assigned to a single group only, finding stable outcomes becomes intractable, even if the underlying network is very simple: the problem of determining whether a given instance of a GASP admits a Nash stable outcome turns out to be NPhard when the social network is a path, a star, or if the size of each connected component is bounded by a constant. On the other hand, we obtain fixed-parameter tractability results for this problem with respect to the number of activities. |
| Researcher Affiliation | Academia | Ayumi Igarashi, Dominik Peters, Edith Elkind Department of Computer Science University of Oxford, UK EMAIL |
| Pseudocode | No | The paper describes algorithms in prose within the proofs of theorems (e.g., Theorem 5), but does not present them in a distinct pseudocode or algorithm block format. |
| Open Source Code | No | The paper mentions an extended version available on arXiv, but does not provide any link or statement regarding the release of source code. |
| Open Datasets | No | The paper is theoretical and does not use or mention any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not use or mention data splits. |
| Hardware Specification | No | The paper is theoretical and does not mention hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details like hyperparameters or training configurations. |