A heuristic combining 2-club edge deletion with vertex splitting achieves the best F-score among tested methods on overlapping LFR benchmark graphs.
On the Complexity of 2-club Cluster Editing with Vertex Splitting
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abstract
Editing a graph to obtain a disjoint union of s-clubs is one of the models for correlation clustering, which seeks a partition of the vertex set of a graph so that elements of each resulting set are close enough according to some given criterion. For example, in the case of editing into s-clubs, the criterion is proximity since any pair of vertices (in an s-club) are within a distance of s from each other. In this work we consider the vertex splitting operation, which allows a vertex to belong to more than one cluster. This operation was studied as one of the parameters associated with the Cluster Editing problem. We study the complexity and parameterized complexity of the s-Club Cluster Edge Deletion with Vertex Splitting and s-Club Cluster Vertex Splitting problems. Both problems are shown to be NP-Complete and APX-hard. On the positive side, we show that both problems are Fixed-Parameter Tractable with respect to the number of allowed editing operations and that s-Club Cluster Vertex Splitting is solvable in polynomial-time on the class of forests.
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cs.SI 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Correlation Clustering with Overlap: a Heuristic Graph Editing Approach
A heuristic combining 2-club edge deletion with vertex splitting achieves the best F-score among tested methods on overlapping LFR benchmark graphs.