Finding modules, or clusters, in networks currently attracts much attention in several domains. The most studied criterion for doing so, due to Newman and Girvan [Phys. Rev. E 69, 026113 (2004)], is modularity maximization. Many heuristics have been proposed for maximizing modularity and yield rapidly near optimal solution or sometimes optimal ones but without a guarantee of optimality. There are few exact algorithms, prominent among which is a paper by Xu [Eur. Phys. J. B 60, 231 (2007)]. Modularity maximization can also be expressed as a clique partitioning problem and the row generation algorithm of Grötschel and Wakabayashi [Math. Program. 45, 59 (1989)] applied. We propose to extend both of these algorithms using the powerful column generation methods for linear and non linear integer programming. Performance of the four resulting algorithms is compared on problems from the literature. Instances with up to 512 entities are solved exactly. Moreover, the computing time of previously solved problems are reduced substantially.
The recently developed Variable Neighborhood Search (VNS) metaheuristic for combinatorial and global optimization is outlined together with its specialization to the problem of finding extremal graphs with respect to one or more invariants and the corresponding program (AGX). We illustrate the potential of the VNS algorithm on the example of energy E, a graph invariant which (in the case of molecular graphs of conjugated hydrocarbons) corresponds to the total π-electron energy. Novel lower and upper bounds for E are suggested by AGX and several conjectures concerning (molecular) graphs with extremal E values put forward. Moreover, most of the bounds are proved to hold.
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