# Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm
## 1 Introduction  
The various characteristic attributes and various rich relational structures contained in complex networks reflect the potential regularity of real systems in the real world. At present, discovering and researching community structures in networks has become a hot topic in complex network research. Community discovery technology mainly studies how to detect and analyze the community structure contained in a large number of complex networks, including discipline theory in the fields of graph theory, data mining, machine learning, statistical analysis and pattern recognition [1]. Discovering and studying the community structure in various types of complex networks is of great value to social life and human practice. Therefore, research on community detection algorithms in large-scale complex networks is an important research topic, both in terms of theoretical significance and practical value.  
From the current research status, as the scale and complexity of the network continue to expand, it is becoming more and more difficult to find a satisfactory community structure throughtraditional community detection algorithms or a single artificial intelligence optimization algorithm. Although some achievements have been made in the improvement and application research of community detection algorithms, in the existing community detection algorithms actually applied in large-scale complex networks, it is often difficult to meet the actual network requirements. Therefore, this paper focuses on the study of community structure in large-scale complex networks. Based on a comparative study of various existing community detection algorithms, this paper proposes a corresponding community detection algorithm, and combines artificial and real network data to verify and analyze the algorithm. The specific research contents include: (1) Introduce the relevant theories of complex networks, and introduce the two community structure quality evaluation indicators used in this paper: network module degree and normalization mutual information (NMI); (2) focus on the analysis of several common community detection algorithms; (3) Introduce the principle of particle swarm optimization algorithm and genetic algorithm, and introduce the corresponding process; (4) Based on particle swarm optimization and genetic algorithm, a hybrid algorithm strategy is proposed. The test function is used to test the single and hybrid algorithms, and the proposed particle swarm-genetic hybrid algorithm improves the overall performance of the algorithm [2,3]. By testing the community detection algorithms on real networks and artificially generated network data sets, it is verified that the particle swarm-genetic algorithm has certain advantages in the accuracy of community detection compared with other algorithms. The results show that combined with the advantages of particle swarm optimization and genetic algorithm, the proposed particle swarm-genetic hybrid algorithm has certain theoretical significance and application value for community exploration research in large-scale complex networks.