Abstract
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms.
Highlights
Many networks can be simulated by complex networks, such as social networks, biological networks, and the World Wide Web
We proposed an evolutionary membrane community detection algorithm based on self-organizing maps (SOM), named EMCD-SOM
There are many metrics for evaluating the effectiveness of community detection algorithms that detect the quality of network partitions of complex networks
Summary
Many networks can be simulated by complex networks, such as social networks, biological networks, and the World Wide Web. The study of complex networks is increasingly attracting the attention of researchers from many different fields. The study of complex networks is increasingly attracting the attention of researchers from many different fields These complex networks are represented by nodes and edges. In order to clearly understand the structural characteristics and functional characteristics of complex networks, finding the relationship between these nodes and edges is especially important for studying the composition of the network and understanding the functional characteristics of the network. As a method to revealing the relationship between nodes and edges in the network, community structure has become a hot research topic in network science. More and more researchers are paying attention to community detection problems in complex networks [1,2,3]
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