Complex network cell discovering method under adaptive evolution bat algorithm for self-media network data

Abstract

The invention provides a complex network cell discovering method under an adaptive evolution bat algorithm for self-media network data. The method includes the following steps that S1, mass data is acquired, a network structure model is established, according to the bat algorithm, a modularity function serves as a fitness function, a coding mode based on characters is adopted, and an initialized population is improved through a label propagation method; S2, the individual speed of the bat algorithm is converted into a mutation probability value, position update is calculated through a cross operator and a mutation operator, therefore, adaptive evolution of the common bat algorithm is achieved, the adaptive evolution bat algorithm is used for dividing a network, and a more accurate network cell division result is obtained. Compared with other intelligent algorithms for cell discovery, the algorithm has the advantages of being high in convergence rate and solution precision, and is more suitable for cell discovery under the large-scale network.

Description

technical field [0001] The invention relates to the field of big data mining, in particular to a method for discovering complex network communities under the self-adaptive evolutionary bat algorithm for self-media network data. Background technique [0002] Complex systems can be modeled as complex networks for analysis, and communities can be viewed as subsystems with certain properties or functions in the system. Community structures exist in many complex networks, and the study of community structures in complex networks has important theoretical significance. First of all, through the community structure, the common characteristics and attributes of the node group can be mined, which is helpful to analyze the relationship between the whole and the part; secondly, based on the existing network information, the functions of similar nodes can be predicted, and the potential relationship between nodes Connection possibility; finally, the impact of community structure on dyn...

AI Technical Summary

Problems solved by technology

The existing technology uses a genetic algorithm based on binary matrix coding. Although this coding is convenient for interleaving operations, the coding and decoding are complicated, and the algorithm time complexity is O(n 3 ), and coding correction is required; the immune cloning operator is added to the discrete differential evolution algorithm, which improves the local development capability of the algorithm and increases the robustness of the algorithm; the prior art proposes a discrete particle swarm optimization algorithm, which can update the speed Redefining and applying community discovery in symbolic networks

Modularity functions suffer from a resolution-limited problem, i.e. such methods do not perform well in the presence of small communities in large-scale networks

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