Benchmark Sets, Training Data and Results for "Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning"
Year: 2026
Type: dataset
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
Institutions: Karlsruhe Institute of Technology, Heidelberg University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7148412851