Benchmark Sets, Training Data and Results for "Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning"
Maas, Nikolai
1; Seemaier, Daniel 1; Langedal, Kenneth; Schrape, Simeon
1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)
1; Seemaier, Daniel 1; Langedal, Kenneth; Schrape, Simeon1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)
Abstract:
set_a.zip: benchmark set of 118 small to medium graphs. All graphs are unweighted and in Metis format
set_b.zip: subset of Set A, consisting of 69 instances, used as a training set for our models
set_b1-20.zip: subset of Set B, consisting of 20 instances with high mean edge label values
set_b21-45.zip: subset of Set B, consisting of 25 instances with medium mean edge label values
set_b46-69.zip: subset of Set B, consisting of 24 instances with low mean edge label values
features_set_b.zip: input features that we used for our models for all graphs of Set B
labels_set_b.zip: training edge labels for all graphs of Set B
benchmark_results.zip: experimental results for all evaluated graph partitioning algorithms
set_b.zip: subset of Set A, consisting of 69 instances, used as a training set for our models
set_b1-20.zip: subset of Set B, consisting of 20 instances with high mean edge label values
set_b21-45.zip: subset of Set B, consisting of 25 instances with medium mean edge label values
set_b46-69.zip: subset of Set B, consisting of 24 instances with low mean edge label values
features_set_b.zip: input features that we used for our models for all graphs of Set B
labels_set_b.zip: training edge labels for all graphs of Set B
benchmark_results.zip: experimental results for all evaluated graph partitioning algorithms
| Zugehörige Institution(en) am KIT | Institut für Theoretische Informatik (ITI) |
| Publikationstyp | Forschungsdaten |
| Publikationsjahr | 2026 |
| Identifikator | KITopen-ID: 1000198980 |
| HGF-Programm | 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups |
| Lizenz | Creative Commons Namensnennung 4.0 International |
| Art der Forschungsdaten | Dataset |
| Nachgewiesen in | OpenAlex |