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..
Enumerating Potential Maximal Cliques via SAT and ASP
Authors: Tuukka Korhonen, Jeremias Berg, Matti Järvisalo
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that integrating the proposed approaches into a recent BT implementation compares favorably with the use of specialized enumeration on the tasks of determining treewidth and generalized hypertreewidth. 6 Experiments We integrated the three declarative approaches to PMC enumeration to the recent BT implementation Triangulator [Korhonen et al., 2019] that supports both treewidth and generalized hypertreewidth. |
| Researcher Affiliation | Academia | Tuukka Korhonen , Jeremias Berg and Matti J arvisalo HIIT, Department of Computer Science, University of Helsinki, Finland |
| Pseudocode | Yes | Algorithm 1 SAT-based lazy enumeration of size-k PMCs |
| Open Source Code | Yes | The implementation is available at https://github.com/Laakeri/pmcenum-ijcai. |
| Open Datasets | Yes | For further experiments, we used all of the benchmarks instances from [Korhonen et al., 2019], consisting of 589 graphs for treewidth and 265 for generalized hypertreewidth gathered from various different sources (including PACE 2016 and 2017 instances). |
| Dataset Splits | No | The paper describes an iterative algorithm that uses a parameter 'k' to enumerate PMCs, but does not provide explicit training, validation, or test dataset splits in the context of machine learning model training. |
| Hardware Specification | Yes | All experiments were run single-threaded on computing nodes with 2.4-GHz Intel Xeon E5-2680-v4 processors. |
| Software Dependencies | Yes | We used Clingo 5.3.0 [Gebser et al., 2016] as the de-facto ASP solver, and based on preliminary experiments of several state-of-the-art SAT solvers Glucose 4.1 as the SAT solver [Audemard and Simon, 2018] through its incremental API. |
| Experiment Setup | Yes | A per-instance 2-hour time limit and 32-GB memory limit was imposed. For ASP, we used Clingo s built-in support for enumerating all answer sets. |