||A community is a group of vertices that share common properties and/or similar roles within the graph. The Community Detection is a process of discovering well-defined communities based on the principle that there are more edges inside a community than edges connecting the rest of the graph. This problem is quite challenging and remains an active research field, recently expanded to Multiplex Networks, which incorporate several channels of connectivity in a system, and provide a natural description for systems in which entities have a different set of neighbors at each layer. This proposal attempts developing new models to detect communities in multiplex networks, thus addressing the key shortcomings of existing models. The proposal comprises two research directions: 1) to improve the Girvan-Newman algorithm by handling the issues with the betweenness measure and next proposing an aggregation operator capable of reducing the information loss, and 2) to propose a new algorithm based on tensor algebra and rough sets to analyze the multiplex network as a single n-dimensional space. In both cases, we will consider entities having different nature, particularly those represented as sets. It should be commented that the proposed algorithms must be able to cover both monoplex and multiplex CD problems, as such approaches suffer from the same shortcomings. The theoretical results of this research will be applied to a real-world problem related to email marketing.