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Discrete Applied Mathematics
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https://dx.doi.org/10.48550/ar...
Article . 2019
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Cluster Editing with Vertex Splitting

Cluster editing with vertex splitting
Authors: Faisal N. Abu-Khzam; Emmanuel Arrighi; Matthias Bentert; Pål Grønås Drange; Judith Egan; Serge Gaspers; Alexis Shaw; +3 Authors

Cluster Editing with Vertex Splitting

Abstract

Cluster Editing, also known as Correlation Clustering, is a well-studied graph modification problem. In this problem, one is given a graph and the task is to perform up to $k$ edge additions or deletions to transform it into a cluster graph, i.e., a graph consisting of a disjoint union of cliques. However, in real-world networks, clusters are often overlapping. For example in social networks, a person might belong to several communities - e.g. those corresponding to work, school, or neighborhood. Other strong motivations come from biological network analysis and from language networks. Trying to cluster words with similar usage in the latter can be confounded by homonyms, that is, words with multiple meanings like "bat." In this paper, we introduce a new variant of Cluster Editing whereby a vertex can be split into two or more vertices. First used in the context of graph drawing, this operation allows a vertex $v$ to be replaced by two vertices whose combined neighborhood is the neighborhood of $v$ (and thus $v$ can belong to more than one cluster). We call the new problem Cluster Editing with Vertex Splitting and we initiate the study of it. We show that it is NP-complete and fixed-parameter tractable when parameterized by the total number $k$ of allowed vertex-splitting and edge-editing operations. In particular, we obtain an $O(2^{9k log k} + n + m)$-time algorithm and a $6k$-vertex kernel.

Keywords

FOS: Computer and information sciences, Combinatorial optimization, parameterized algorithms, Parameterized complexity, tractability and kernelization, Programming involving graphs or networks, Computational Complexity (cs.CC), Computer Science - Computational Complexity, vertex splitting, Graph theory (including graph drawing) in computer science, Graph algorithms (graph-theoretic aspects), cluster editing, graph modification, kernelization, Computer Science - Data Structures and Algorithms, NP-hardness, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), Data Structures and Algorithms (cs.DS)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
Green
hybrid