Open Access

Peer-reviewed Research Article

European Journal of Artificial Intelligence, Jun 14, 2020 |

Compact homeomorphisms of semantic groups

Main Article Content


The document looks at the identifying community on social networks. There is a graphical approach to the study of social networks. There is a comparative analysis of the basic algorithms and the aggregated al-algorithm proposed by the authors. To test the algorithms, the authors initially generated graphs with different noise levels and gave a common number. To compare the shares of the graph, two well-known metrics that the authors used were Normal Output Mutual Information (NMI) and Split Separation. Each of the indicators has its advantages. To check the basic algorithms and analyze the authors of the geographical social network Facebook for the presence of the community in them and test the aggregate algorithm of MetaClust. The proposed MetaClust algorithm showed high performance compared to the basic ones. The values ​​of the modularity of its shares (average) are higher compared to the main algorithms. Also, the quality of the algorithm can be judged by the lack of "tail" modularity in the distribution. The average results shown by the algorithms on the generated graphs correspond to the results of the application in ego networks. It seems appropriate to use pre-fractional graphs and a wider class of dynamic graphs to generate model data. The sequence of the generated community graphs corresponds to the dynamic trajectory of the graphs, the communities are seeds and blocks, and the noise is the addition of the new end of different ranks between the seeds. The next step is a formal description of the noise of the graphs in the class terminology of the dynamic and pre-fractal graphs


Article Details

How to Cite
Raychev, N. (2020). Compact homeomorphisms of semantic groups. European Journal of Artificial Intelligence, 1(1), 77-92.
Author Biography

Nikolay Raychev