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..
Object Allocation via Swaps along a Social Network
Authors: Laurent Gourvès, Julien Lesca, Anaëlle Wilczynski
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the complexity of these problems by providing, according to the structure of the social network, polynomial and NP-complete cases. We prove that the problem is NP-complete even when the network is a tree. Proposition 1 When G is a star, there exists a polynomial algorithm for Reachable Object. |
| Researcher Affiliation | Academia | Laurent Gourv es, Julien Lesca and Ana elle Wilczynski Univ. Paris-Dauphine, PSL Research University, CNRS, LAMSADE, Paris, France |
| Pseudocode | Yes | Algorithm 1: Input: (N, X, , G, σ0), assignment σ Output: whether σ is reachable from σ0. Algorithm 2: Input: (N, X, , G, σ0), agent k Output: an assignment σ |
| Open Source Code | No | The paper makes no mention of open-source code availability for the described methodology and does not provide any links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no information about publicly available training data is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing, as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes algorithms and theoretical concepts but does not mention any specific software dependencies or versions for implementation, as it doesn't conduct experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setups, hyperparameters, or training configurations. |