Research On Network Community Discovery Based On Chemical Reaction Algorithm To Optimize Society Score

With the popularity and application of the Internet,the network is spread all over the life.For example,the existence of social networks,human social behavior is affected.People are like a node,connected by the Internet,and each node seems to be independent of each other,but there is a certain connection.It is these points and edges that form a complex network,and the relationship between points and edges forms the topology of the network.The complex structure has a great influence on the community structure.It is precisely because of the community structure that we can deeply study some behaviors in the network,which can help people discover the laws existing in complex networks and explain social relationships and phenomena.Community testing is an important means of discovering the structure of a community.In recent years,the research of community detection algorithms has become an important branch of complex networks.Researchers have proposed a variety of intelligent algorithms,such as genetic algorithms,particle swarm algorithms,firefly algorithms.However,those algorithms have some limitations,which make the algorithm time complexity too high,the algorithm fall into local optimum,and the efficiency of the algorithm is low.In this paper,a new heuristic chemical reaction algorithm is used,and the community score is used as the fitness function of the algorithm.A community discovery algorithm is proposed,which can solve the limitation of the algorithm to some extent.The main research work and innovations of this paper are:1.The classical community discovery algorithm,chemical reaction algorithm and the evaluation function of the community in complex networks in complex networks are studied.The structure of the community is defined as the internal nodes are tightly connected and the external nodes are sparsely connected.and the module degree is proposed to describe the characteristics of the community structure;the module density is proposed in solving the problem of modular resolution.Discovering the community structure is the focus of this paper,so this paper also studies some commonly used community detection algorithms.2.A community detection method based on chemical reaction algorithm to optimize community scores is proposed.The proposed algorithm is based on the chemical reaction algorithm,and the community score is fitness function.The problem of community detection is transformed into the problem of finding the minimum potential energy.The four primary responses through optimize the community score to find the lowest potential energy.3.The label propagation method is adopted as a method of population initialization.In order to improve the diversity and effectiveness of the initial population,the introduction of label propagation method makes the algorithm more Precision and stable,so that the community structure is more obvious and the division effect is better.4.Introduce the tabu search algorithm as a local search operator.In order to improve the local search ability of the algorithm optimization,the chemical reaction algorithm is combined with the tabu search algorithm,which can improve the optimization ability of the algorithm and speed up the convergence of the algorithm.Experiments on artificial synthetic networks and real networks show that the proposed algorithm has better segmentation results on both networks,and has a more obvious community structure.Compared with the other five Compared algorithms,the algorithm has a community structure.obvious advantage.