关于某些图聚类测度的NP完全性
On the NP-Completeness of Some Graph Cluster Measures
摘要：
图聚类是识别给定图中稀疏连接的稠密子图（簇）的问题。提出的聚类算法通常会优化各种适应度函数，这些适应度函数用于衡量图中聚类的质量。这种集群措施的例子包括电导率，局部和相对密度，和单簇编辑。我们证明了与优化任务相关联的决策问题，找到最佳的集群，这些健身措施是NP-完全的。
英文摘要：
Graph clustering is the problem of identifying sparsely connected dense subgraphs (clusters) in a given graph. Proposed clustering algorithms usually optimize various fitness functions that measure the quality of a cluster within the graph. Examples of such cluster measures include the conductance, the local and relative densities, and single cluster editing. We prove that the decision problems associated with the optimization tasks of finding the clusters that are optimal with respect to these fitness measures are NP-complete.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
