Branch-and-Cut Algorithms for Colorful Components Problems
Claudia Archetti, Martina Cerulli, Carmine Sorgente
INFORMS Journal on Computing · 2026 · Volume 38 · Issue 3
Abstract
We tackle three optimization problems in which a colored graph, where each node is assigned a color, must be partitioned into colorful connected components. A component is defined as colorful if each color appears at most once. The problems differ in the objective function, which determines which partition is the best one. These problems have applications in community detection, cybersecurity, and bioinformatics. We present integer nonlinear formulations, which are then linearized using standard techniques. To solve these formulations, we develop exact branch-and-cut algorithms, embedding various improving techniques, such as valid inequalities, bounds limiting the number of variables, and warm-start and preprocessing techniques. Extensive computational tests on benchmark instances demonstrate the effectiveness of the proposed procedures. The branch-and-cut algorithms can solve reasonably sized instances efficiently. To the best of our knowledge, we are the first to propose an exact algorithm for solving these problems. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms & Applications. Funding: The work of M. Cerulli was partially funded by project “SEcurity and RIghts in the CyberSpace” [Grant PE00000014] under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0927 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0927 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Keywords
graphs, colorful components, bioinformatics, social networks
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