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..
Approximating Optimal Labelings for Temporal Connectivity
Authors: Daniele Carnevale, Gianlorenzo D'Angelo, Martin Olsen
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results. We provide both hardness of approximation lower-bounds and approximation algorithms for MAL. Hardness of approximation. We first prove that, even when the maximum allowed age a of a labeling is a fixed value greater or equal to 2, MAL cannot be approximated within a factor better than O(log n), unless P = NP. Then, we show that, unless NP DTIME(2polylog(n)), we cannot find any 2log1 ϵ n-approximation algorithm for MAL, even when a is a fixed value greater or equal to 3 and ϵ (0, 1). These results advance our knowledge on the computational complexity of MAL in two directions. |
| Researcher Affiliation | Academia | 1Gran Sasso Science Institute, L Aquila, Italy 2Aarhus University, Aarhus, Denmark EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithmic steps and procedures, particularly in the 'Proof sketch of Theorem 8' section, but does not present them in a formal, structured pseudocode block or algorithm environment. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | No | The paper describes theoretical proofs and approximation algorithms for graph problems (Minimum Aged Labeling, Set Cover, MIN-REP) using constructed graph instances (e.g., 'Let (U, C) be an instance of SC. We construct a graph G = (V, E) which we will use to prove Theorem 2.'). It does not use or refer to any publicly available empirical datasets. |
| Dataset Splits | No | The paper describes theoretical proofs and approximation algorithms for graph problems and does not involve empirical evaluation on datasets, hence dataset splits are not applicable. |
| Hardware Specification | No | The paper presents theoretical complexity results and approximation algorithms; it does not describe any experimental evaluation requiring specific hardware. |
| Software Dependencies | No | The paper focuses on theoretical complexity and algorithm design, not experimental implementation, so no software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical, focusing on complexity and approximation algorithms, and does not include any experimental setup details or hyperparameters. |