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The integration that maximized the modularity score was used to determine the number of communities.
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The merging process is stopped either by setting the expected number of clusters, or by bounding the maximal cluster's cardinality or by maximizing the modularity.
The last three algorithms intend to maximize the modularity, denoted by Q.
The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity.
An open question is how to maximize the modularity of their design to enable the integration of different reaction networks and to optimize their scalability and flexibility.
In this approach we use an evolutionary algorithm to find the first community structure that maximizes the modularity.
In the merging process, subgroup pairs are iteratively merged to identify the final community structure maximizing the modularity.
In this method we use genetic algorithm to split a network into two networks which maximize the modularity.
In the second step we propose a strategy to merge these initial groups to identify the final community structure (with k communities or the structure that maximizes the modularity in Community Detection Based on Node Similarity and Modularity Q (ComDBNSQ)).
In this paper, a Memetic algorithm (MA) based on genetic algorithm with two different local search strategies is proposed to maximize the modularity density, and a more general version of the objective function is used with a tunable parameter λ which can resolve the resolution limit.
Different heuristic approaches have been proposed for computing partitions that maximize the modularity.
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