The Parameterized Complexity of Network Microaggregation

Microaggregation is a classical statistical disclosure control technique which requires the input data to be partitioned into clusters while adhering to specified size constraints. We provide novel exact algorithms and lower bounds for the task of microaggregating a given network while considering both unrestricted and connected clusterings, and analyze these from the perspective of the parameterized complexity paradigm. Altogether, our results assemble a complete complexity-theoretic picture for the network microaggregation problem with respect to the most natural parameterizations of the problem, including input-specified parameters capturing the size and homogeneity of the clusters as well as the treewidth and vertex cover number of the network.

Authors

• Václav Blažej Faculty of Information Technology, Czech Technical University in Prague, Prague, Czechia
• Robert Ganian Algorithms and Complexity Group, TU Wien, Vienna, Austria
• Dušan Knop Czech Technical University in Prague
• Jan Pokorný Czech Technical University in Prague
• Šimon Schierreich AGH University of Krakow
• Kirill Simonov Hasso Plattner Institute

Keywords

• CSO: Other Foundations of Constraint Satisfaction & Optimization
• DMKM: Graph Mining, Social Network Analysis & Community Mining
• GTEP: Other Foundations of Game Theory & Economic Paradigms
• KRR: Computational Complexity of Reasoning
• ML: Clustering