АbstractIn this paper, we propose and implement a method for detecting intersecting and nested communities in graphs of interacting objects of diff erent natures. For this, two classical algorithms are taken: a hierarchical agglomerate and one based on the search for k-cliques. The combined algorithm presented is based on their consistent application. In addition, parametric options are developed that are responsible for actions with communities whose sizes are smaller than the given k, and also with single vertices. Varying these parameters allows us to take into account diff erences in the topology of the original graph and thus to correct the algorithm.The testing was carried out on real data, including on a group of graphs of a social network, and the qualitative content of the resulting partition was investigated. To assess the diff erences between the integrated method and the classical algorithms of community detections, a common measure of similarity was used. As a result, it is clearly shown that the resulting partitions are signifi cantly diff erent. We found that for the approach proposed in the article the index of the numerical characteristic of the partitioning accuracy, modularity, can be lower than the corresponding value for other approaches. At the same time, the result of an integrated method is often more informative due to intersections and nested community structure.A visualization of the partition obtained for one of the examples by an integrated method at the fi rst and last steps is presented. Along with the successfully found set of parameters of the integrated method for small communities and cut off vertices in the case of social networks, some shortcomings of the proposed model are noted. Proposals are made to develop this approach by using a set of parametric algorithms.
The purpose of the study:. search for a technique for constructing and analyzing a graph of interacting objects in the network of Telegram channels, including the calculation of psycholinguistic characteristics of texts. This tech- nique makes it possible to classify groups of channels and evaluate their informational impact on users.Method:. U , M , R -model is used to build a weighted graph during data import. Next, on the resulting graph,the Galaxies method is applied to reveal implicit intersecting communities. Psycholinguistic factors are calculated on the imported combined texts of communities to assess the channels thematic focus. Results:. the article presents a methodology for working with a network of Telegram channels in order to identify groups of channels that carry out information impact on users. A full cycle of actions is presented, starting from data import, using a model for constructing a graph of interacting objects for such networks, and ending with the calculation of psycholinguistic characteristics of texts for groups of channels. At the same time, the issue of the most effective selection of implicit communities in networks of Telegram channels is highlighted. An example of a network and a constructed weighted graph with markers calculated on texts, which are the most indicative for identifying the channels focus, is presented. The presented approach, by highlighting significant differences in the corresponding markers, makes it possible to identify channels that most actively carry out informational impact on users. The combination of an algorithmic approach and the use of psycholinguistic research represent the scien- tific novelty of this method. The results obtained make it possible, using the methods of computational linguistics in combination with the communities reveal methods, to evaluate different participants in such networks.
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