The Complexity of Cluster Vertex Splitting and Company

TL;DR

This paper investigates the computational complexity of overlapping graph clustering via vertex splitting, showing NP-hardness results and providing a kernelization approach for splitting a limited number of vertices.

Contribution

It establishes NP-hardness for cluster vertex splitting and related problems, and introduces a kernelization method for splitting at most k vertices.

Findings

Covering and vertex-splitting viewpoints are equivalent.

Splitting at most k vertices admits an O(k)-vertex kernel.

NP-hardness of Cluster Editing with Vertex Splitting is proven.

Abstract

Clustering a graph when the clusters can overlap can be seen from three different angles: We may look for cliques that cover the edges of the graph with bounded overlap, we may look to add or delete few edges to uncover the cluster structure, or we may split vertices to separate the clusters from each other. Splitting a vertex $v$ means to remove it and to add two new copies of $v$ and to make each previous neighbor of $v$ adjacent with at least one of the copies. In this work, we study underlying computational problems regarding the three angles to overlapping clusterings, in particular when the overlap is small. We show that the above-mentioned covering problem is NP-complete. We then make structural observations that show that the covering viewpoint and the vertex-splitting viewpoint are equivalent, yielding NP-hardness for the vertex-splitting problem. On the positive side, we show that splitting at most $k$ vertices to obtain a cluster graph has a problem kernel with $O(k)$ vertices. Finally, we observe that combining our hardness results with the so-called critical-clique lemma yields NP-hardness for Cluster Editing with Vertex Splitting, which was previously open (Abu-Khzam et al. [ISCO 2018]) and independently shown to be NP-hard by Arrighi et al. [IPEC 2023]. We observe that a previous version of the critical-clique lemma was flawed; a corrected version has appeared in the meantime on which our hardness result is based.