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Graph clustering is the problem of identifying sparsely connected dense subgraphs (clusters) in a given graph. Identifying clusters can be achieved by optimizing a fitness function that measures 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 clusters that are optimal with respect to these fitness measures are NP-complete. [ABSTRACT FROM AUTHOR]

The important task of generating the minimum number of sequential triangle strips for a given triangulated surface model is motived by applications in computer graphics. This hard combinatorial optimization problem is reduced to the minimum energy problem in Hopfield nets by a linear-size construction. First practical experiments have confirmed that computing the semi-optimal stripifications by using Hopfield nets is a promising approach. In this work we provide a theoretical justification of this method by proving that the classes of equivalent optimal stripifications are mapped one to one to the minimum energy states. [ABSTRACT FROM AUTHOR]