# Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm
## 2 Prior Research  
Research topics on community detection have always been a hot topic for scholars, attracting researchers in many disciplines, including mathematics, sociology, physics, statistics, computer science and biology. In the process of research, new methods or theories about the exploration of complex network community structure are constantly being proposed, and the relevant research results are also fruitful. With the deepening of research, many principles and algorithms for complex network community detection have been developed. In [4], the authors propose an effective overlapping community detection algorithm using the seed expansion method. The algorithmic idea is to find good seeds and then greedily expand these seeds according to community indicators. A new seeding strategy was developed for the personalized PageRank clustering scheme, which optimizes the conductivity community score. In [5], the author introduces two new algorithms, LPAwb+ and DIRTLPAwb+, to maximize the weighting module in the two-party network. LPAwb+ and DIRTLPAwb+ can reliably identify partitions with higher modular scores. In [6], the author proposes a Spark-based parallel incremental dynamic community detection algorithm (PIDCDS), which can maximize the total persistence of vertices in the network to discover the community structure. PIDCDS uses parallel computing on GraphX to calculate the special permanent as a community partitioning indicator. In [7], the author proposes an image segmentation algorithm based on graph-based community detection. The image is represented as a non-directional weighted graph on which community detection is performed, with each community corresponding to a segment in the image. In [8], in order to speed up the optimization process and more effectively discover multiple important community structures, the authors propose a multi-objective evolutionary algorithm that uses problem-specific genetic variation and population crossing and problem-specific initialization to evaluate. In [9], for most existing methods, including some methods to convert the problem into a classic setting of community detection in a simplex network, the author proposes a new method, which includes a seed-centric algorithm adaptation. In [10], the authors propose a novel multi-target bat algorithm that uses the mean shift algorithm to generate an initial population to obtain a high-quality solution. By giving the natural bat speed, frequency, loudness and pulsation rate another meaning, the algorithm's operators are applied to community detection problems in social dynamic networks. For particle swarm optimization, it has the advantages of fast convergence, high precision and the advantages of genetic algorithm with random global search ability and good generalization ability. Many researchers at home and abroad have applied them in different fields. In [11], the authors used a particle swarm optimization-based approach to train neural networks (NN-PSO) and applied it to building structure prediction. The NN-PSO classifier can solve the problem of predicting failure of multilayer-reinforced concrete building structures by detecting the failure probability of future multilayer RC building structures. In [12], the authors applied PSO to deep brain stimulation (DBS) treatment. The DBSA was programmed by developing a particle swarm optimization algorithm to use a single population of particles representing the electrode configuration and stimulus amplitude. The algorithm provides a computationally efficient way to program DBS systems, especially for systems with higher electrode counts. In [13], in order to solve the location management problem, the author proposes a binary particle swarm optimization algorithm (BPSO) using the optimal reporting cell planning technique, in order to reduce the management costs of position generated during the tracking process of locating users to the cellular network. In [14], the authors propose particle swarm optimization for trajectory optimization.