The Parameterized Complexity of Network Microaggregation

Authors

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

DOI:

https://doi.org/10.1609/aaai.v37i5.25771

Keywords:

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

Abstract

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.
AAAI-23 Proceedings Cover

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Published

2023-06-26

How to Cite

Blažej, V., Ganian, R., Knop, D., Pokorný, J., Schierreich, Šimon, & Simonov, K. (2023). The Parameterized Complexity of Network Microaggregation. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6262-6270. https://doi.org/10.1609/aaai.v37i5.25771

Issue

Vol. 37 No. 5: AAAI-23 Technical Tracks 5

Section

AAAI Technical Track on Knowledge Representation and Reasoning