Simulated Annealing Algorithm for Community Detection in Complex Networks
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In complex network community detection algorithms, the simulated annealing optimization method can be employed to enhance partition quality. Simulated annealing is a stochastic search-based optimization algorithm that explores the solution space by mimicking the annealing process in metallurgy, gradually reducing the system temperature to converge toward optimal solutions. For implementation, MATLAB provides robust numerical computation and optimization capabilities that facilitate algorithm development and testing. The implementation typically involves defining an objective function (such as modularity Q), generating initial partitions, and iteratively exploring neighboring solutions while controlling temperature parameters. Key MATLAB functions used may include rand() for random number generation, graph object manipulation for network representation, and custom functions for calculating energy states and acceptance probabilities during the annealing process.
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