Fixed-parameter tractability of graph modification problems for hereditary properties
Information Processing Letters
Cluster graph modification problems
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Invitation to data reduction and problem kernelization
ACM SIGACT News
Detecting Critical Regions in Covert Networks: A Case Study of 9/11 Terrorists Network
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
A More Relaxed Model for Graph-Based Data Clustering: s-Plex Editing
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Parameterized graph cleaning problems
Discrete Applied Mathematics
Fixed-Parameter Algorithms for Cluster Vertex Deletion
Theory of Computing Systems - Special Section: Algorithmic Game Theory; Guest Editors: Burkhard Monien and Ulf-Peter Schroeder
A kernelization algorithm for d-Hitting Set
Journal of Computer and System Sciences
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem
Operations Research
A More Relaxed Model for Graph-Based Data Clustering: $s$-Plex Cluster Editing
SIAM Journal on Discrete Mathematics
Parameterized approximation via fidelity preserving transformations
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
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We introduce the NP-hard graph-based data clustering problem s-Plex Cluster Vertex Deletion, where the task is to delete at most k vertices from a graph so that the connected components of the resulting graph are s-plexes. In an s-plex, every vertex has an edge to all but at most s−1 other vertices; cliques are 1-plexes. We propose a new method for kernelizing a large class of vertex deletion problems and illustrate it by developing an O(k2s3)-vertex problem kernel for s-Plex Cluster Vertex Deletion that can be computed in O(ksn2) time, where n is the number of graph vertices. The corresponding “kernelization through tidying” exploits polynomial-time approximation results.