使用差分进化 确定社交网络中的社区结构_python_Jupyter _代码_下载

# DETERMINING COMMUNITY STRUCTURE IN SOCIAL NETWORK USING DIFFERENTIAL EVOLUTION Research on determining community structure in complex networks has attracted a lot of attention in various applications, such as e-mail networks, citation networks, social networks, metabolic networks, airline networks, biological networks, information networks, technology networks, and computer networks. The popularity of determining the community structure is because it can analyze the structure and functionality of a network, where the network or community itself can be interpreted as a node that is closely related in an information network. Meanwhile, it is difficult to determine a community structure by maximizing the value of modularity. Therefore, many studies have introduced new algorithms to solve problems in determining community structure and maximizing the value of this modularity. Differential Evolution is an evolution-based algorithm that is similar to the genetic algorithm (GA) in which DE is very simple and efficient. DE can provide speed, and accuracy solutions, which are very suitable for detection of a community. DE does not need to know about community structure and number of communities to be able to detect a community. This research focuses on Differential Evolution based Community Detection (DECD) which adds a clean up feature in it. The final result of this research is the comparison of the modularity value based on the community structure determination from DECD, Girvan and Newman Algorithm and Louvain Algorithm. The best results for modularity were obtained using DECD which got 0.6833 for the Zachary's karate club dataset, 0.7446 for the Bootlenose dolphins dataset, 0.7242 for the American college football dataset, and 0.5892 for the Books about US politics dataset. # Dataset Karate 0.6833  # Dataset Dolphins 0.7446  # Dataset Football 0.7242  # Dataset Books 0.5892