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Uma Abordagem para Deteccão de Comunidades a partir de Sequências de Interacões Sociais
Belo Horizonte, MG, dissertation, (24.04.2018)
Аннотация
The topology of a social network and the temporal aspect of the interactions between a pair of vertices indicate the strength of the relationship between them and allow to classify it. For example, a relationship can be classified as persistent and embedded based, respectively, on the regularity with which interactions occur and on the number of common neighbors of the pair of vertices involved. On the other hand, a rare and less embedded relationship is generally random and represents noise in a social network, hiding the most significant structure of the network and preventing an accurate analysis. In this dissertation, we propose a framework to handle social network data that exploits temporal and topological features of its sequences of real and synthetic interactions to improve the detection of static communities by existing algorithms. By removing random relationships, we verified by means of multiple sources of evidence that in more than 80 percent of the cases, the social networks converge to a topology with more purely social relationships and higher quality community structures.
