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Competitive Strategies for Online Clique Clustering

机译:在线Clique聚类的竞争策略

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A clique clustering of a graph is a partitioning of its vertices into disjoint cliques. The quality of a clique clustering is measured by the total number of edges in its cliques. We consider the online variant of the clique clustering problem, where the vertices of the input graph arrive one at a time. At each step, the newly arrived vertex forms a singleton clique, and the algorithm can merge any existing cliques in its partitioning into larger cliques, but splitting cliques is not allowed. We give an online strategy with competitive ratio 15.645 and we prove a lower bound of 6 on the competitive ratio, improving the previous respective bounds of 31 and 2.
机译:图形的Clique聚类是其顶点的分区成差异派系。通过其群体中的边缘总数来测量Clique聚类的质量。我们考虑Clique聚类问题的在线变体,其中输入图的顶点一次到达。在每个步骤中,新到达的顶点形成单例Clique,并且该算法可以将其分区中的任何现有的派系合并到较大的派系中,但不允许分割派系。我们提供具有竞争力的在线策略15.645,我们证明了6个竞争比率的下限,改善了之前的31和2的各自范围。

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