On 2-Clubs in Graph-Based Data Clustering: Theory and Algorithm Engineering

CIAC(2021)

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摘要
Editing a graph into a disjoint union of clusters is a standard optimization task in graph-based data clustering. Here, complementing classic work where the clusters shall be cliques, we focus on clusters that shall be 2-clubs, that is, subgraphs of diameter at most two. This naturally leads to the two NP-hard problems 2-Club Cluster Editing (the editing operations are edge insertion and edge deletion) and 2-Club Cluster Vertex Deletion (the editing operations are vertex deletions). Answering an open question, we show that 2-Club Cluster Editing is W[2]-hard with respect to the number of edge modifications, thus contrasting the fixed-parameter tractability result for the classic Cluster Editing problem (considering cliques instead of 2-clubs). Then, focusing on 2-Club Cluster Vertex Deletion, which is easily seen to be fixed-parameter tractable, we show that under standard complexity-theoretic assumptions it does not have a polynomial-size problem kernel when parameterized by the number of vertex deletions. Nevertheless, we develop several effective data reduction and pruning rules, resulting in a competitive solver, outperforming a standard CPLEX solver in most instances of an established biological test data set.
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关键词
data clustering,algorithm,graph-based
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