Título: An alternative class of models to position social network groups in latent spaces
Autores: Izabel Nolau, Gustavo S Ferreira
Resumo: Identifying key nodes, estimating the probability of connection between them, and distinguishing latent groups are some of the main objectives of social network analysis. In this paper, we propose a class of blockmodels to model stochastic equivalence and visualize groups in an unobservable space. In this setting, the proposed method is based on two approaches: latent distances and latent dissimilarities at the group level. The projection proposed in the paper is performed without needing to project individuals, unlike the main approaches in the literature. Our approach can be used in undirected or directed graphs and is flexible enough to cluster and quantify between and withingroup tie probabilities in social networks. The effectiveness of the methodology in representing groups in latent spaces was analyzed under artificial datasets and in a case study
Palavras-chave: blockmodel, social networks, multidimensional scaling, latent space, visualization
Páginas: 8
Código DOI: 10.21528/CBIC2023-036
Artigo em pdf: CBIC_2023_paper036.pdf
Arquivo BibTeX: CBIC_2023_036.bib